Binder

ODE parameter prediction

We will perform ODE parameter prediction for forecasting of the number of cases. We have two ways for prediction.

  1. Time-series prediction withOUT indicators

  2. Time-series prediction with indicators

The second one uses indicators, including OxCGRT indicators, the number of vaccinations.

Note:
The target (Y) of prediction is ODE parameter values, not the number of cases. ODE parameter values are more useful because ODE parameter values have physical meanings, including (non-dimensional) effective contact rate, and they are always in the range of (0, 1).
[1]:

from datetime import timedelta
import covsirphy as cs
from matplotlib import pyplot as plt
import numpy as np
cs.__version__

[1]:

'3.1.2.delta'

The target of prediction is estimated ODE parameter values. At we will prepare them as explained with tutorials. For example, we can use class method ODEScenario.auto_build(), specifying location name. “Baseline” scenario will be created automatically with downloaded datasets.

[2]:

snr = cs.ODEScenario.auto_build(geo="Japan", model=cs.SIRFModel)
# Show summary
snr.summary()

2025-12-15 at 13:39:08 | INFO | 
<SIR-F Model: parameter estimation>
2025-12-15 at 13:39:08 | INFO | Running optimization with 4 CPUs...

2025-12-15 at 13:41:22 | INFO | Completed optimization. Total: 2 min 13 sec


[2]:
Start End Rt theta kappa rho sigma alpha1 [-] 1/alpha2 [day] 1/beta [day] 1/gamma [day] ODE tau
Scenario Phase
Baseline 0th 2020-02-23 2020-08-10 1.32 0.048768 0.00003 0.002101 0.001482 0.049 560 8 11 SIR-F Model 24
1st 2020-08-11 2020-11-16 0.81 0.000074 0.000014 0.000853 0.001037 0.0 1225 20 16 SIR-F Model 24
2nd 2020-11-17 2020-12-24 1.75 0.007356 0.000007 0.00109 0.00061 0.007 2365 15 27 SIR-F Model 24
3rd 2020-12-25 2021-01-16 1.52 0.000565 0.000014 0.001188 0.00077 0.001 1190 14 22 SIR-F Model 24
4th 2021-01-17 2021-02-10 0.7 0.007165 0.000013 0.000636 0.000891 0.007 1256 26 19 SIR-F Model 24
... ... ... ... ... ... ... ... ... ... ... ... ... ...
63rd 2023-03-12 2023-03-27 1.65 0.000441 0.000003 0.001116 0.000673 0.0 6399 15 25 SIR-F Model 24
64th 2023-03-28 2023-04-07 0.63 0.000078 0.000005 0.000969 0.001543 0.0 3226 17 11 SIR-F Model 24
65th 2023-04-08 2023-04-18 1.43 0.000221 0.000003 0.001742 0.001213 0.0 6594 10 14 SIR-F Model 24
66th 2023-04-19 2023-04-27 1.66 0.000088 0.000005 0.001943 0.001166 0.0 3575 9 14 SIR-F Model 24
67th 2023-04-28 2023-05-08 1.55 0.000123 0.000002 0.001512 0.000971 0.0 7871 11 17 SIR-F Model 24

68 rows × 13 columns

For demonstration, we will get the start date of future phases.

[3]:

future_start_date = snr.simulate(display=False).index.max() + timedelta(days=1)
future_start_date

[3]:

Timestamp('2023-05-09 00:00:00')

1. Time-series prediction withOUT indicators

This scenario “Predicted” does not use indicators, using AutoTS package: a time series package for Python designed for rapidly deploying high-accuracy forecasts at scale.

At first, create “Predicted” scenario, copying estimated ODE parameter values of “Baseline” scenario.

[4]:

snr.build_with_template(name="Predicted", template="Baseline");

Then, run ODEScenario().predict(days, name, **kwargs). We can apply keyword arguments of autots.AutoTS() except for forecast_length (always the same as days).

[5]:

snr.predict(days=30, name="Predicted");

Check the predicted ODE parameter values.

[6]:

df = snr.append().summary()
df.loc[df["Start"] >= future_start_date]

[6]:
Start End Rt theta kappa rho sigma alpha1 [-] 1/alpha2 [day] 1/beta [day] 1/gamma [day] ODE tau
Scenario Phase
Predicted 68th 2023-05-10 2023-06-07 1.18 0.000447 0.000003 0.001512 0.001274 0.0 4767 11 13 SIR-F Model 24

Check the dynamics.

[7]:

snr.simulate(name="Predicted");
_images/06_prediction_15_0.png

2. Time-series prediction with indicators

using `future_regressor functionality of AutoTS <https://winedarksea.github.io/AutoTS/build/html/source/tutorial.html#adding-regressors-and-other-information>`__, we will predict ODE parameter values with indicators. We can download/create time-series data of indicators using DataEngineer class as explained in Tutorial: Data preparation and Tutorial: Data engineering.

[8]:

data_eng = cs.DataEngineer()
data_eng.download(databases=["japan", "covid19dh", "owid"]).clean().transform()
subset_df, *_ = data_eng.subset(geo="Japan")
indicator_df = subset_df.drop(["Population", "Susceptible", "Confirmed", "Infected", "Fatal", "Recovered"], axis=1)
indicator_df

[8]:
Cancel_events Contact_tracing Gatherings_restrictions Information_campaigns Internal_movement_restrictions International_movement_restrictions School_closing Stay_home_restrictions Stringency_index Testing_policy ... Transport_closing Vaccinated_full Vaccinated_once Vaccinations Vaccinations_boosters Workplace_closing Country_0 Country_Japan Product_0 Product_Moderna, Novavax, Oxford/AstraZeneca, Pfizer/BioNTech
Date
2020-02-05 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 1 0 1 0
2020-02-06 0.0 2.0 0.0 2.0 0.0 3.0 0.0 0.0 19.44 1.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0 1 1 0
2020-02-07 0.0 2.0 0.0 2.0 0.0 3.0 0.0 0.0 19.44 1.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0 1 1 0
2020-02-08 0.0 2.0 0.0 2.0 0.0 3.0 0.0 0.0 19.44 1.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0 1 1 0
2020-02-09 0.0 2.0 0.0 2.0 0.0 3.0 0.0 0.0 19.44 1.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0 1 1 0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
2023-05-04 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 33.33 1.0 ... 0.0 63889986991.0 67171434888.0 181897083005.0 50835661126.0 1.0 0 1 0 1
2023-05-05 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 33.33 1.0 ... 0.0 63993367323.0 67276140121.0 182280782416.0 51011274972.0 1.0 0 1 0 1
2023-05-06 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 33.33 1.0 ... 0.0 64096748099.0 67380845596.0 182664482513.0 51186888818.0 1.0 0 1 0 1
2023-05-07 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 33.33 1.0 ... 0.0 64200128969.0 67485551123.0 183048182756.0 51362502664.0 1.0 0 1 0 1
2023-05-08 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 33.33 1.0 ... 0.0 64303509961.0 67590256732.0 183431912857.0 51538146164.0 1.0 0 1 0 1

1189 rows × 21 columns

2.1 Principal Component Analysis

To remove multicollinearity of indicators, we use pca package: a python package to perform Principal Component Analysis and to create insightful plots via our MLEngineer(seed=0).pca(X, n_components). Standardization (Z-score normalization) and Principal Component Analysis (PCA) will be performed.

[9]:

ml = cs.MLEngineer()
pca_dict = ml.pca(X=indicator_df, n_components=0.95)
pca_df = pca_dict["PC"].copy()
pca_df.tail()

[15-12-2025 13:41:40] [pca.pca] [INFO] Extracting column labels from dataframe.
[15-12-2025 13:41:40] [pca.pca] [INFO] Extracting row labels from dataframe.
[15-12-2025 13:41:40] [pca.pca] [INFO] Normalizing input data per feature (zero mean and unit variance)..
[15-12-2025 13:41:40] [pca.pca] [INFO] PCA reduction performed to capture at least 95.0% explained variance using 21 columns of the input data.
[15-12-2025 13:41:40] [pca.pca] [INFO] Fit using PCA.
[15-12-2025 13:41:40] [pca.pca] [INFO] Compute loadings and PCs.
[15-12-2025 13:41:40] [pca.pca] [INFO] Compute explained variance.
[15-12-2025 13:41:40] [pca.pca] [INFO] The top 10 principal component(s) explains >= 95.00% of the explained variance.
[15-12-2025 13:41:40] [pca.pca] [INFO] The PCA reduction is performed on 21 variables (columns) of the input dataframe.
[15-12-2025 13:41:40] [pca.pca] [INFO] Fit using PCA.
[15-12-2025 13:41:40] [pca.pca] [INFO] Compute loadings and PCs.
[15-12-2025 13:41:40] [pca.pca] [INFO] Outlier detection using Hotelling T2 test with alpha=[0.05] and n_components=[10]
[15-12-2025 13:41:40] [pca.pca] [INFO] Multiple test correction applied for Hotelling T2 test: [fdr_bh]
[15-12-2025 13:41:40] [pca.pca] [INFO] Outlier detection using SPE/DmodX with n_std=[3]

[9]:
PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 PC9 PC10
2023-05-04 5.673492 -2.243826 0.555292 -0.134307 0.888876 0.020971 -0.364962 -0.630145 0.039768 -0.324168
2023-05-05 5.683267 -2.247735 0.557146 -0.134422 0.891652 0.019268 -0.363681 -0.632074 0.041499 -0.325286
2023-05-06 5.693041 -2.251644 0.558999 -0.134538 0.894427 0.017564 -0.362401 -0.634004 0.043231 -0.326404
2023-05-07 5.702815 -2.255553 0.560853 -0.134654 0.897203 0.015860 -0.361120 -0.635933 0.044962 -0.327522
2023-05-08 5.712591 -2.259462 0.562707 -0.134770 0.899979 0.014157 -0.359839 -0.637862 0.046694 -0.328641

The output of MLEngineer().pca() is the model of pca package, we can show some figures easily as follows.

Explained variance:

[10]:

pca_dict["model"].plot()
plt.close()

Top features:

[11]:

df = pca_dict["topfeat"].copy()
df["PC"] = df["PC"].str.extract(r"(\d+)").astype(np.int64)
df = df.sort_values(["PC", "type"]).reset_index(drop=True)

def highlight(d):
    styles = d.copy()
    styles.loc[:, :] = ""
    styles.loc[d["type"] == "best", :] = "background-color: yellow"
    return styles

df.style.apply(highlight, axis=None)

[11]:
  PC feature loading type
0 1 Vaccinated_once 0.347669 best
1 1 Contact_tracing -0.294071 weak
2 1 Information_campaigns -0.281398 weak
3 1 Vaccinated_full 0.346382 weak
4 1 Vaccinations 0.343559 weak
5 1 Vaccinations_boosters 0.308128 weak
6 2 Stringency_index 0.438532 best
7 2 International_movement_restrictions 0.313789 weak
8 2 Product_0 -0.318462 weak
9 2 Product_Moderna, Novavax, Oxford/AstraZeneca, Pfizer/BioNTech 0.318462 weak
10 3 Country_Japan 0.677881 best
11 3 Country_0 -0.677881 weak
12 4 School_closing 0.558851 best
13 5 Transport_closing 0.554676 best
14 6 Workplace_closing 0.650936 best
15 7 Tests 0.570970 best
16 8 Cancel_events 0.513032 best
17 9 Testing_policy 0.664159 best
18 10 Gatherings_restrictions 0.525608 best
19 10 Internal_movement_restrictions 0.331885 weak
20 10 Stay_home_restrictions -0.421741 weak

2-2. Future values of indicators

Before prediction of ODE parameter values, we need to prepare future values of (PCA-performed) indicators. We can add future values to the pandas.DataFrame manually or forecast them with MLEngineer(seed=0).predict(Y, days=<int>, X=None) as follows.

[12]:

future_df = ml.forecast(Y=pca_df, days=30, X=None)
future_df.tail()

Using 1 cpus for n_jobs.
Data frequency is: D, used frequency is: D
Model Number: 1 with model AverageValueNaive in generation 0 of 1 with params {"method": "Mean"} and transformations {"fillna": "fake_date", "transformations": {"0": "DifferencedTransformer", "1": "SinTrend"}, "transformation_params": {"0": {}, "1": {}}}
Model Number: 2 with model AverageValueNaive in generation 0 of 1 with params {"method": "Mean"} and transformations {"fillna": "mean", "transformations": {"0": "ClipOutliers", "1": "QuantileTransformer", "2": "DifferencedTransformer"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 3, "fillna": null}, "1": {"output_distribution": "uniform", "n_quantiles": 1000}, "2": {}}}
Model Number: 3 with model AverageValueNaive in generation 0 of 1 with params {"method": "Mean"} and transformations {"fillna": "rolling_mean_24", "transformations": {"0": "SeasonalDifference", "1": "Round", "2": "Detrend"}, "transformation_params": {"0": {"lag_1": 7, "method": "Mean"}, "1": {"model": "middle", "decimals": 2, "on_transform": true, "on_inverse": false}, "2": {"model": "GLS"}}}
Model Number: 4 with model GLS in generation 0 of 1 with params {} and transformations {"fillna": "median", "transformations": {"0": "ClipOutliers", "1": "QuantileTransformer", "2": "RobustScaler", "3": "Round", "4": "MaxAbsScaler"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 3.5, "fillna": null}, "1": {"output_distribution": "uniform", "n_quantiles": 1000}, "2": {}, "3": {"model": "middle", "decimals": 2, "on_transform": true, "on_inverse": true}, "4": {}}}
Model Number: 5 with model LastValueNaive in generation 0 of 1 with params {} and transformations {"fillna": "rolling_mean_24", "transformations": {"0": "PositiveShift", "1": "SinTrend", "2": "bkfilter"}, "transformation_params": {"0": {}, "1": {}, "2": {}}}
Model Number: 6 with model LastValueNaive in generation 0 of 1 with params {} and transformations {"fillna": "fake_date", "transformations": {"0": "SeasonalDifference", "1": "SinTrend"}, "transformation_params": {"0": {"lag_1": 7, "method": "LastValue"}, "1": {}}}
Model Number: 7 with model LastValueNaive in generation 0 of 1 with params {} and transformations {"fillna": "mean", "transformations": {"0": "ClipOutliers", "1": "QuantileTransformer"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 1, "fillna": null}, "1": {"output_distribution": "uniform", "n_quantiles": 1000}}}
Model Number: 8 with model SeasonalNaive in generation 0 of 1 with params {"method": "LastValue", "lag_1": 2, "lag_2": 1} and transformations {"fillna": "rolling_mean_24", "transformations": {"0": "SeasonalDifference", "1": "QuantileTransformer", "2": "Detrend"}, "transformation_params": {"0": {"lag_1": 12, "method": "Median"}, "1": {"output_distribution": "uniform", "n_quantiles": 1000}, "2": {"model": "GLS"}}}
Model Number: 9 with model SeasonalNaive in generation 0 of 1 with params {"method": "LastValue", "lag_1": 7, "lag_2": 2} and transformations {"fillna": "mean", "transformations": {"0": "QuantileTransformer", "1": "ClipOutliers"}, "transformation_params": {"0": {"output_distribution": "uniform", "n_quantiles": 1000}, "1": {"method": "clip", "std_threshold": 2, "fillna": null}}}
Model Number: 10 with model ConstantNaive in generation 0 of 1 with params {} and transformations {"fillna": "ffill", "transformations": {"0": "PowerTransformer", "1": "QuantileTransformer", "2": "SeasonalDifference"}, "transformation_params": {"0": {}, "1": {"output_distribution": "uniform", "n_quantiles": 1000}, "2": {"lag_1": 7, "method": "LastValue"}}}
Model Number: 11 with model SeasonalNaive in generation 0 of 1 with params {"method": "lastvalue", "lag_1": 364, "lag_2": 28} and transformations {"fillna": "ffill", "transformations": {"0": "SeasonalDifference", "1": "MaxAbsScaler", "2": "Round"}, "transformation_params": {"0": {"lag_1": 7, "method": "LastValue"}, "1": {}, "2": {"decimals": 0, "on_transform": false, "on_inverse": true}}}
Model Number: 12 with model SectionalMotif in generation 0 of 1 with params {"window": 10, "point_method": "weighted_mean", "distance_metric": "sokalmichener", "include_differenced": true, "k": 20, "stride_size": 1, "regression_type": null} and transformations {"fillna": "zero", "transformations": {"0": null}, "transformation_params": {"0": {}}}
Model Number: 13 with model SectionalMotif in generation 0 of 1 with params {"window": 5, "point_method": "midhinge", "distance_metric": "canberra", "include_differenced": false, "k": 10, "stride_size": 1, "regression_type": null} and transformations {"fillna": "rolling_mean_24", "transformations": {"0": "QuantileTransformer", "1": "QuantileTransformer", "2": "RobustScaler"}, "transformation_params": {"0": {"output_distribution": "uniform", "n_quantiles": 1000}, "1": {"output_distribution": "uniform", "n_quantiles": 1000}, "2": {}}}
Model Number: 14 with model SeasonalNaive in generation 0 of 1 with params {"method": "lastvalue", "lag_1": 364, "lag_2": 30} and transformations {"fillna": "fake_date", "transformations": {"0": "SeasonalDifference", "1": "MaxAbsScaler", "2": "Round"}, "transformation_params": {"0": {"lag_1": 7, "method": "LastValue"}, "1": {}, "2": {"decimals": 0, "on_transform": false, "on_inverse": true}}}
Model Number: 15 with model SeasonalityMotif in generation 0 of 1 with params {"window": 5, "point_method": "weighted_mean", "distance_metric": "mae", "k": 10, "datepart_method": "common_fourier"} and transformations {"fillna": "nearest", "transformations": {"0": "AlignLastValue"}, "transformation_params": {"0": {"rows": 1, "lag": 1, "method": "multiplicative", "strength": 1.0, "first_value_only": false}}}
Model Number: 16 with model SectionalMotif in generation 0 of 1 with params {"window": 7, "point_method": "median", "distance_metric": "canberra", "include_differenced": true, "k": 5, "stride_size": 1, "regression_type": null} and transformations {"fillna": "ffill", "transformations": {"0": "SeasonalDifference", "1": "AlignLastValue", "2": "SeasonalDifference", "3": "LevelShiftTransformer"}, "transformation_params": {"0": {"lag_1": 7, "method": "Median"}, "1": {"rows": 1, "lag": 2, "method": "additive", "strength": 1.0, "first_value_only": false}, "2": {"lag_1": 12, "method": 5}, "3": {"window_size": 30, "alpha": 2.0, "grouping_forward_limit": 4, "max_level_shifts": 10, "alignment": "average"}}}
Model Number: 17 with model BasicLinearModel in generation 0 of 1 with params {"datepart_method": "simple_binarized", "changepoint_spacing": 360, "changepoint_distance_end": 360, "regression_type": null, "lambda_": 0.01} and transformations {"fillna": "ffill_mean_biased", "transformations": {"0": "Round", "1": "AlignLastValue", "2": "HistoricValues", "3": "ClipOutliers", "4": "bkfilter"}, "transformation_params": {"0": {"decimals": -1, "on_transform": true, "on_inverse": true}, "1": {"rows": 7, "lag": 1, "method": "additive", "strength": 0.2, "first_value_only": true, "threshold": null, "threshold_method": "mean"}, "2": {"window": 10}, "3": {"method": "clip", "std_threshold": 3, "fillna": null}, "4": {}}}
Template Eval Error: Traceback (most recent call last):
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 2230, in TemplateWizard
    df_forecast = model_forecast(
                  ^^^^^^^^^^^^^^^
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 1562, in model_forecast
    model = model.fit(df_train_low, future_regressor_train)
            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 895, in fit
    self.model = self.model.fit(
                 ^^^^^^^^^^^^^^^
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/models/basics.py", line 3789, in fit
    np.linalg.inv(X_values.T @ X_values + self.lambda_ * I)
ValueError: matmul: Input operand 1 has a mismatch in its core dimension 0, with gufunc signature (n?,k),(k,m?)->(n?,m?) (size 1159 is different from 2424)
 in model 17 in generation 0: BasicLinearModel
Model Number: 18 with model BasicLinearModel in generation 0 of 1 with params {"datepart_method": ["dayofweek", [365.25, 14]], "changepoint_spacing": 90, "changepoint_distance_end": 360, "regression_type": null, "lambda_": null, "trend_phi": 0.98} and transformations {"fillna": "piecewise_polynomial", "transformations": {"0": "AlignLastValue", "1": "IntermittentOccurrence", "2": "RobustScaler", "3": "Log"}, "transformation_params": {"0": {"rows": 1, "lag": 2, "method": "multiplicative", "strength": 0.9, "first_value_only": false, "threshold": 1, "threshold_method": "mean"}, "1": {"center": "mean"}, "2": {}, "3": {}}}
Model Number: 19 with model SeasonalityMotif in generation 0 of 1 with params {"window": 5, "point_method": "trimmed_mean_40", "distance_metric": "mae", "k": 5, "datepart_method": ["simple_binarized_poly"], "independent": true} and transformations {"fillna": "rolling_mean", "transformations": {"0": "Constraint", "1": "QuantileTransformer", "2": "AlignLastValue"}, "transformation_params": {"0": {"constraint_method": "historic_diff", "constraint_direction": "lower", "constraint_regularization": 1.0, "constraint_value": 0.2, "bounds_only": false, "fillna": null}, "1": {"output_distribution": "uniform", "n_quantiles": 43}, "2": {"rows": 1, "lag": 1, "method": "additive", "strength": 0.2, "first_value_only": false, "threshold": null, "threshold_method": "max"}}}
Model Number: 20 with model BasicLinearModel in generation 0 of 1 with params {"datepart_method": "recurring", "changepoint_spacing": 28, "changepoint_distance_end": 90, "regression_type": null, "lambda_": 0.01, "trend_phi": 0.98, "holiday_countries_used": true} and transformations {"fillna": "ffill", "transformations": {"0": "Slice", "1": "ClipOutliers", "2": "SeasonalDifference", "3": "LevelShiftTransformer", "4": "LevelShiftTransformer", "5": "PositiveShift"}, "transformation_params": {"0": {"method": 0.2}, "1": {"method": "clip", "std_threshold": 3, "fillna": null}, "2": {"lag_1": 12, "method": 5}, "3": {"window_size": 7, "alpha": 2.0, "grouping_forward_limit": 5, "max_level_shifts": 30, "alignment": "rolling_diff_3nn"}, "4": {"window_size": 7, "alpha": 2.0, "grouping_forward_limit": 5, "max_level_shifts": 30, "alignment": "rolling_diff_3nn"}, "5": {}}}
Model Number: 21 with model ConstantNaive in generation 0 of 1 with params {"constant": 1} and transformations {"fillna": "zero", "transformations": {"0": "ClipOutliers", "1": "Detrend", "2": "LevelShiftTransformer"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 3, "fillna": null}, "1": {"model": "GLS", "phi": 1, "window": 30, "transform_dict": null}, "2": {"window_size": 7, "alpha": 3.5, "grouping_forward_limit": 6, "max_level_shifts": 40, "alignment": "rolling_diff_3nn", "output": "univariate", "remove_at_shift": true, "shift_remove_window": 2, "shift_fillna": "linear", "window_method": "diff_overlap"}}}
Model Number: 22 with model LastValueNaive in generation 0 of 1 with params {} and transformations {"fillna": "rolling_mean", "transformations": {"0": "StandardScaler", "1": "HPFilter", "2": "AlignLastValue", "3": "Constraint", "4": "ChangepointDetrend"}, "transformation_params": {"0": {}, "1": {"part": "trend", "lamb": 104976000000}, "2": {"rows": 1, "lag": 7, "method": "additive", "strength": 1.0, "first_value_only": false, "threshold": 1, "threshold_method": "mean", "mean_type": "arithmetic"}, "3": {"constraint_method": "quantile", "constraint_direction": "upper", "constraint_regularization": 0.7, "constraint_value": 1.0, "bounds_only": false, "fillna": null}, "4": {"model": "Linear", "changepoint_spacing": 5040, "changepoint_distance_end": 5040, "datepart_method": "simple_2"}}}
Model Number: 23 with model AverageValueNaive in generation 0 of 1 with params {"method": "Median", "window": 42} and transformations {"fillna": "seasonal_linear", "transformations": {"0": "StandardScaler", "1": "ScipyFilter", "2": "AlignLastValue", "3": "ScipyFilter"}, "transformation_params": {"0": {}, "1": {"method": "butter", "method_args": {"N": 8, "window_size": 51, "btype": "highpass", "analog": false, "output": "sos"}}, "2": {"rows": 1, "lag": 1, "method": "additive", "strength": 0.9, "first_value_only": true, "threshold": 10, "threshold_method": "max", "mean_type": "arithmetic"}, "3": {"method": "butter", "method_args": {"N": 7, "window_size": 96, "btype": "lowpass", "analog": false, "output": "sos"}}}}
Model Number: 24 with model GLS in generation 0 of 1 with params {"changepoint_spacing": 60, "changepoint_distance_end": 28, "constant": true} and transformations {"fillna": "ffill", "transformations": {"0": "MeanDifference", "1": "ThetaTransformer", "2": "EWMAFilter", "3": "ClipOutliers", "4": "AlignLastValue", "5": "CointegrationTransformer"}, "transformation_params": {"0": {}, "1": {"theta_values": [0.8, 1.2]}, "2": {"span": 3}, "3": {"method": "clip", "std_threshold": 2, "fillna": null}, "4": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false, "threshold": 10, "threshold_method": "max", "mean_type": "arithmetic"}, "5": {"n_components": 5, "max_components": 5, "shrinkage": 1e-06, "method": "cca", "min_periods": 15}}}
Template Eval Error: Traceback (most recent call last):
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 8428, in inverse_transform
    df = self._inverse_one(
         ^^^^^^^^^^^^^^^^^^
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 8353, in _inverse_one
    df = self.transformers[i].inverse_transform(
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 3352, in inverse_transform
    self.adjustment.abs() <= self.threshold,
    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/pandas/core/ops/common.py", line 76, in new_method
    return method(self, other)
           ^^^^^^^^^^^^^^^^^^^
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/pandas/core/arraylike.py", line 52, in __le__
    return self._cmp_method(other, operator.le)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/pandas/core/series.py", line 6133, in _cmp_method
    raise ValueError("Can only compare identically-labeled Series objects")
ValueError: Can only compare identically-labeled Series objects

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 2230, in TemplateWizard
    df_forecast = model_forecast(
                  ^^^^^^^^^^^^^^^
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 1563, in model_forecast
    return model.predict(
           ^^^^^^^^^^^^^^
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 924, in predict
    df_forecast = self.transformer_object.inverse_transform(df_forecast)
                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 8397, in inverse_transform
    prediction.forecast = self.inverse_transform(
                          ^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 8435, in inverse_transform
    raise Exception(err_str) from e
Exception: Transformer AlignLastValue failed on inverse from params ffill {'0': {}, '1': {'theta_values': [0.8, 1.2]}, '2': {'span': 3}, '3': {'method': 'clip', 'std_threshold': 2, 'fillna': None}, '4': {'rows': 1, 'lag': 1, 'method': 'additive', 'strength': 1.0, 'first_value_only': False, 'threshold': 10, 'threshold_method': 'max', 'mean_type': 'arithmetic'}, '5': {'n_components': 5, 'max_components': 5, 'shrinkage': 1e-06, 'method': 'cca', 'min_periods': 15}} with ValueError('Can only compare identically-labeled Series objects')
 in model 24 in generation 0: GLS
Model Number: 25 with model SeasonalNaive in generation 0 of 1 with params {"method": "lastvalue", "lag_1": 75, "lag_2": 24} and transformations {"fillna": "ffill", "transformations": {"0": "ClipOutliers"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 1, "fillna": null}}}
Model Number: 26 with model SeasonalityMotif in generation 0 of 1 with params {"window": 50, "point_method": "mean", "distance_metric": "mae", "k": 1, "datepart_method": "common_fourier", "independent": true} and transformations {"fillna": "akima", "transformations": {"0": "AlignLastValue"}, "transformation_params": {"0": {"rows": 7, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false, "threshold": null, "threshold_method": "max", "mean_type": "arithmetic"}}}
Model Number: 27 with model SectionalMotif in generation 0 of 1 with params {"window": 30, "point_method": "median", "distance_metric": "yule", "include_differenced": true, "k": 20, "stride_size": 2, "regression_type": null, "comparison_transformation": null, "combination_transformation": null} and transformations {"fillna": "linear", "transformations": {"0": "PCA", "1": "PCA", "2": "Slice", "3": "StandardScaler", "4": "FIRFilter", "5": "AlignLastValue"}, "transformation_params": {"0": {"whiten": true, "n_components": null}, "1": {"whiten": false, "n_components": 100}, "2": {"method": 0.2}, "3": {}, "4": {"numtaps": 7, "cutoff_hz": 0.5, "window": "hamming", "sampling_frequency": 28, "on_transform": true, "on_inverse": false, "bounds_only": false}, "5": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false, "threshold": 10, "threshold_method": "max", "mean_type": "arithmetic"}}}
Template Eval Error: Traceback (most recent call last):
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 8295, in _fit
    df = self._fit_one(df, i)
         ^^^^^^^^^^^^^^^^^^^^
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 8279, in _fit_one
    df = self.transformers[i].fit_transform(df)
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 2655, in fit_transform
    return self._fit(df)
           ^^^^^^^^^^^^^
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 2595, in _fit
    return_df = self.transformer.fit_transform(df)
                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/sklearn/utils/_set_output.py", line 316, in wrapped
    data_to_wrap = f(self, X, *args, **kwargs)
                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/sklearn/base.py", line 1336, in wrapper
    return fit_method(estimator, *args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/sklearn/decomposition/_pca.py", line 466, in fit_transform
    U, S, _, X, x_is_centered, xp = self._fit(X)
                                    ^^^^^^^^^^^^
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/sklearn/decomposition/_pca.py", line 540, in _fit
    return self._fit_full(X, n_components, xp, is_array_api_compliant)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/sklearn/decomposition/_pca.py", line 554, in _fit_full
    raise ValueError(
ValueError: n_components=100 must be between 0 and min(n_samples, n_features)=10 with svd_solver='covariance_eigh'

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 2230, in TemplateWizard
    df_forecast = model_forecast(
                  ^^^^^^^^^^^^^^^
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 1562, in model_forecast
    model = model.fit(df_train_low, future_regressor_train)
            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 887, in fit
    df_train_transformed = self.transformer_object._fit(df)
                           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 8300, in _fit
    raise Exception(err_str) from e
Exception: Transformer PCA failed on fit from params linear {'0': {'whiten': True, 'n_components': None}, '1': {'whiten': False, 'n_components': 100}, '2': {'method': 0.2}, '3': {}, '4': {'numtaps': 7, 'cutoff_hz': 0.5, 'window': 'hamming', 'sampling_frequency': 28, 'on_transform': True, 'on_inverse': False, 'bounds_only': False}, '5': {'rows': 1, 'lag': 1, 'method': 'additive', 'strength': 1.0, 'first_value_only': False, 'threshold': 10, 'threshold_method': 'max', 'mean_type': 'arithmetic'}} with error ValueError("n_components=100 must be between 0 and min(n_samples, n_features)=10 with svd_solver='covariance_eigh'")
 in model 27 in generation 0: SectionalMotif
Model Number: 28 with model BasicLinearModel in generation 0 of 1 with params {"datepart_method": "common_fourier_rw_lag", "changepoint_spacing": 5040, "changepoint_distance_end": 5040, "regression_type": null, "lambda_": null, "trend_phi": null, "holiday_countries_used": false} and transformations {"fillna": "linear", "transformations": {"0": "RollingMeanTransformer", "1": "SeasonalDifference", "2": "DifferencedTransformer", "3": "AlignLastValue", "4": "cffilter"}, "transformation_params": {"0": {"fixed": false, "window": 12, "macro_micro": false, "center": false, "mean_type": "arithmetic"}, "1": {"lag_1": 7, "method": "Mean"}, "2": {"lag": 1, "fill": "bfill"}, "3": {"rows": 1, "lag": 1, "method": "additive", "strength": 0.5, "first_value_only": false, "threshold": null, "threshold_method": "max", "mean_type": "arithmetic"}, "4": {}}}
Model Number: 29 with model SectionalMotif in generation 0 of 1 with params {"window": 3, "point_method": "weighted_mean", "distance_metric": "braycurtis", "include_differenced": true, "k": 100, "stride_size": 1, "regression_type": null, "comparison_transformation": {"fillna": "zero", "transformations": {"0": "StandardScaler"}, "transformation_params": {"0": {}}}, "combination_transformation": null} and transformations {"fillna": "quadratic", "transformations": {"0": "ClipOutliers"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 3.5, "fillna": null}}}
Model Number: 30 with model BasicLinearModel in generation 0 of 1 with params {"datepart_method": "recurring", "changepoint_spacing": 5040, "changepoint_distance_end": 520, "regression_type": null, "lambda_": null, "trend_phi": null, "holiday_countries_used": false} and transformations {"fillna": "cubic", "transformations": {"0": "CumSumTransformer"}, "transformation_params": {"0": {}}}
Model Number: 31 with model ConstantNaive in generation 0 of 1 with params {"constant": 0} and transformations {"fillna": "quadratic", "transformations": {"0": "G726Filter", "1": "BKBandpassFilter", "2": "QuantileTransformer"}, "transformation_params": {"0": {"quant_bits": 4, "adaptation_rate": 0.9641121887264618, "prediction_alpha": 0.9437317406672192, "floor_step": 0.03272677759007476, "dynamic_range": 1.244898525944512, "blend": 0.16137863511954614, "noise_gate": 0.020784410369082476, "fill_method": "interpolate", "on_transform": true, "on_inverse": false, "quantizer": "uniform", "use_adaptive_predictor": true, "predictor_leak": 0.9996500337746135, "bounds_only": false}, "1": {"low": 7, "high": 32, "K": 3, "lanczos_factor": true, "return_diff": true, "on_transform": true, "on_inverse": false}, "2": {"output_distribution": "normal", "n_quantiles": 1000}}}
Model Number: 32 with model GLS in generation 0 of 1 with params {"changepoint_spacing": 60, "changepoint_distance_end": null, "constant": true} and transformations {"fillna": "ffill", "transformations": {"0": "CenterSplit"}, "transformation_params": {"0": {"fillna": "ffill", "center": "zero"}}}
Model Number: 33 with model SectionalMotif in generation 0 of 1 with params {"window": 15, "point_method": "weighted_mean", "distance_metric": "mahalanobis", "include_differenced": true, "k": 10, "stride_size": 1, "regression_type": null, "comparison_transformation": null, "combination_transformation": null} and transformations {"fillna": "mean", "transformations": {"0": "ClipOutliers"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 5, "fillna": null}}}
Model Number: 34 with model SeasonalityMotif in generation 0 of 1 with params {"window": 5, "point_method": "closest", "distance_metric": "mae", "k": 1, "datepart_method": "expanded_binarized", "independent": false} and transformations {"fillna": "rolling_mean", "transformations": {"0": "PositiveShift", "1": "Constraint", "2": "AnomalyRemoval", "3": "AlignLastValue", "4": "DifferencedTransformer"}, "transformation_params": {"0": {}, "1": {"constraint_method": "quantile", "constraint_direction": "upper", "constraint_regularization": 0.9, "constraint_value": 0.7, "bounds_only": true, "fillna": null}, "2": {"method": "IQR", "method_params": {"iqr_threshold": 3.0, "iqr_quantiles": [0.4, 0.6]}, "fillna": "mean", "transform_dict": null, "isolated_only": false, "on_inverse": false}, "3": {"rows": 1, "lag": 1, "method": "additive", "strength": 0.2, "first_value_only": false, "threshold": 3, "threshold_method": "max", "mean_type": "arithmetic"}, "4": {"lag": 2, "fill": "bfill"}}}
Model Number: 35 with model ConstantNaive in generation 0 of 1 with params {"constant": 0} and transformations {"fillna": "mean", "transformations": {"0": "bkfilter", "1": "AlignLastValue", "2": "FFTDecomposition", "3": "CenterLastValue", "4": "Constraint"}, "transformation_params": {"0": {}, "1": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false, "threshold": 3, "threshold_method": "max", "mean_type": "arithmetic"}, "2": {"n_harmonics": "mid20", "detrend": "cubic"}, "3": {"rows": 3}, "4": {"constraint_method": "quantile", "constraint_direction": "upper", "constraint_regularization": 1.0, "constraint_value": 0.9, "bounds_only": false, "fillna": null}}}
Model Number: 36 with model SeasonalityMotif in generation 0 of 1 with params {"window": 5, "point_method": "weighted_mean", "distance_metric": "mae", "k": 15, "datepart_method": "recurring", "independent": false} and transformations {"fillna": "ffill", "transformations": {"0": "DatepartRegression"}, "transformation_params": {"0": {"regression_model": {"model": "DecisionTree", "model_params": {"max_depth": 9, "min_samples_split": 1.0}}, "datepart_method": ["db2_365.25_12_0.5", "morlet_7_7_1"], "polynomial_degree": null, "transform_dict": null, "holiday_countries_used": false, "lags": null, "forward_lags": null}}}
Model Number: 37 with model SeasonalNaive in generation 0 of 1 with params {"method": "lastvalue", "lag_1": 13, "lag_2": 28} and transformations {"fillna": "seasonal_linear_window_3", "transformations": {"0": "ClipOutliers", "1": "Detrend", "2": "HistoricValues", "3": "AlignLastValue"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 5, "fillna": null}, "1": {"model": "Linear", "phi": 1, "window": 365, "transform_dict": null}, "2": {"window": 364}, "3": {"rows": 1, "lag": 7, "method": "additive", "strength": 1.0, "first_value_only": false, "threshold": 1, "threshold_method": "max", "mean_type": "arithmetic"}}}
Model Number: 38 with model SectionalMotif in generation 0 of 1 with params {"window": 7, "point_method": "median", "distance_metric": "mahalanobis", "include_differenced": true, "k": 3, "stride_size": 1, "regression_type": "User", "comparison_transformation": null, "combination_transformation": null} and transformations {"fillna": "rolling_mean_24", "transformations": {"0": "DiffSmoother", "1": "AlignLastValue", "2": "MaxAbsScaler", "3": "AlignLastValue"}, "transformation_params": {"0": {"method": null, "method_params": null, "transform_dict": null, "reverse_alignment": true, "isolated_only": false, "fillna": 1.5}, "1": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false, "threshold": 10, "threshold_method": "max", "mean_type": "arithmetic"}, "2": {}, "3": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false, "threshold": 10, "threshold_method": "mean", "mean_type": "arithmetic"}}}
Template Eval Error: Traceback (most recent call last):
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 2230, in TemplateWizard
    df_forecast = model_forecast(
                  ^^^^^^^^^^^^^^^
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 1562, in model_forecast
    model = model.fit(df_train_low, future_regressor_train)
            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 895, in fit
    self.model = self.model.fit(
                 ^^^^^^^^^^^^^^^
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/models/basics.py", line 2007, in fit
    raise ValueError(
ValueError: regression_type=='User' but no future_regressor supplied
 in model 38 in generation 0: SectionalMotif
Model Number: 39 with model ConstantNaive in generation 0 of 1 with params {"constant": 1} and transformations {"fillna": "ffill_mean_biased", "transformations": {"0": "AlignLastValue", "1": "MeanPercentSplitter", "2": "AlignLastValue"}, "transformation_params": {"0": {"rows": 1, "lag": 7, "method": "multiplicative", "strength": 1.0, "first_value_only": false, "threshold": 10, "threshold_method": "mean", "mean_type": "geometric"}, "1": {"window": 3, "min_mean_threshold": 0.1}, "2": {"rows": 7, "lag": 168, "method": "additive", "strength": 1.0, "first_value_only": false, "threshold": null, "threshold_method": "max", "mean_type": "arithmetic"}}}
Model Number: 40 with model BasicLinearModel in generation 0 of 1 with params {"datepart_method": "recurring", "changepoint_spacing": 60, "changepoint_distance_end": null, "regression_type": "User", "lambda_": 0.01, "trend_phi": null, "holiday_countries_used": false} and transformations {"fillna": "ffill", "transformations": {"0": "AnomalyRemoval", "1": "AlignLastValue", "2": "bkfilter", "3": "Log", "4": "StandardScaler", "5": "bkfilter"}, "transformation_params": {"0": {"method": "rolling_zscore", "method_params": {"distribution": "cauchy", "alpha": 0.2, "rolling_periods": 300, "center": true}, "fillna": "rolling_mean_24", "transform_dict": null, "isolated_only": false, "on_inverse": true}, "1": {"rows": 1, "lag": 1, "method": "additive", "strength": 0.5, "first_value_only": false, "threshold": 10, "threshold_method": "max", "mean_type": "arithmetic"}, "2": {}, "3": {}, "4": {}, "5": {}}}
Template Eval Error: Traceback (most recent call last):
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 2230, in TemplateWizard
    df_forecast = model_forecast(
                  ^^^^^^^^^^^^^^^
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 1562, in model_forecast
    model = model.fit(df_train_low, future_regressor_train)
            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 895, in fit
    self.model = self.model.fit(
                 ^^^^^^^^^^^^^^^
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/models/basics.py", line 3767, in fit
    raise ValueError(
ValueError: regression_type=='User' but no future_regressor supplied
 in model 40 in generation 0: BasicLinearModel
Model Number: 41 with model AverageValueNaive in generation 0 of 1 with params {"method": "Mean", "window": null} and transformations {"fillna": "pchip", "transformations": {"0": "ClipOutliers"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 4, "fillna": null}}}
Model Number: 42 with model AverageValueNaive in generation 0 of 1 with params {"method": "Weighted_Mean", "window": null} and transformations {"fillna": "ffill", "transformations": {"0": "StandardScaler", "1": "SeasonalDifference", "2": "ChangepointDetrend", "3": "RobustScaler"}, "transformation_params": {"0": {}, "1": {"lag_1": 12, "method": 20}, "2": {"model": "Linear", "changepoint_spacing": 5040, "changepoint_distance_end": 180, "datepart_method": "simple_binarized"}, "3": {}}}
Model Number: 43 with model AverageValueNaive in generation 0 of 1 with params {"method": "Weighted_Mean", "window": null} and transformations {"fillna": "ffill", "transformations": {"0": "G711Scaler", "1": "HolidayTransformer"}, "transformation_params": {"0": {"mode": "mu", "mu": 50.0, "A": 87.6, "center": "median", "scale_method": "mad", "scale_factor": 2.3395707557081504, "min_scale": 1e-06, "clip": true, "zero_offset": 0.0, "fill_method": "ffill", "on_transform": true, "on_inverse": true, "bounds_only": false}, "1": {"threshold": 0.9, "splash_threshold": null, "use_dayofmonth_holidays": true, "use_wkdom_holidays": true, "use_wkdeom_holidays": false, "use_lunar_holidays": false, "use_lunar_weekday": false, "use_islamic_holidays": false, "use_hebrew_holidays": false, "use_hindu_holidays": false, "anomaly_detector_params": {"method": "rolling_zscore", "method_params": {"distribution": "chi2", "alpha": 0.05, "rolling_periods": 300, "center": false}, "fillna": "linear", "transform_dict": null, "isolated_only": false, "on_inverse": false}, "remove_excess_anomalies": true, "impact": "regression", "regression_params": {}}}}
Model Number: 44 with model ConstantNaive in generation 0 of 1 with params {"constant": 0} and transformations {"fillna": "ffill", "transformations": {"0": "AlignLastValue"}, "transformation_params": {"0": {"rows": 4, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false, "threshold": null, "threshold_method": "max", "mean_type": "arithmetic"}}}
Model Number: 45 with model LastValueNaive in generation 0 of 1 with params {} and transformations {"fillna": "rolling_mean_24", "transformations": {"0": "ClipOutliers", "1": "Detrend", "2": "LevelShiftTransformer", "3": "AlignLastValue"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 3.5, "fillna": null}, "1": {"model": "GLS", "phi": 0.999, "window": null, "transform_dict": null}, "2": {"window_size": 90, "alpha": 2.5, "grouping_forward_limit": 5, "max_level_shifts": 5, "alignment": "last_value", "output": "univariate", "remove_at_shift": false, "shift_remove_window": 1, "shift_fillna": "linear", "window_method": "diff_overlap"}, "3": {"rows": 1, "lag": 7, "method": "additive", "strength": 1.0, "first_value_only": false, "threshold": 1, "threshold_method": "max", "mean_type": "arithmetic"}}}
Model Number: 46 with model AverageValueNaive in generation 0 of 1 with params {"method": "Median", "window": 28} and transformations {"fillna": "ffill_mean_biased", "transformations": {"0": "SinTrend"}, "transformation_params": {"0": {}}}
Model Number: 47 with model SeasonalNaive in generation 0 of 1 with params {"method": "mean", "lag_1": 288, "lag_2": 364} and transformations {"fillna": "nearest", "transformations": {"0": "EWMAFilter", "1": "AlignLastValue"}, "transformation_params": {"0": {"span": 28}, "1": {"rows": 1, "lag": 84, "method": "additive", "strength": 1.0, "first_value_only": false, "threshold": null, "threshold_method": "max", "mean_type": "arithmetic"}}}
Model Number: 48 with model LastValueNaive in generation 0 of 1 with params {} and transformations {"fillna": "ffill", "transformations": {"0": "AlignLastValue"}, "transformation_params": {"0": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false, "threshold": 1, "threshold_method": "mean", "mean_type": "arithmetic"}}}
Model Number: 49 with model SeasonalityMotif in generation 0 of 1 with params {"window": 10, "point_method": "closest", "distance_metric": "mae", "k": 1, "datepart_method": ["weekdaymonthofyear", "quarter", "dayofweek"], "independent": false} and transformations {"fillna": "ffill", "transformations": {"0": "ClipOutliers", "1": "Detrend", "2": "UpscaleDownscaleTransformer", "3": "AlignLastValue"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 2, "fillna": null}, "1": {"model": "Linear", "phi": 1, "window": 90, "transform_dict": null}, "2": {"mode": "upscale", "factor": 1, "down_method": "mean", "fill_method": "ffill"}, "3": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false, "threshold": 1, "threshold_method": "max", "mean_type": "arithmetic"}}}
Model Number: 50 with model GLS in generation 0 of 1 with params {"changepoint_spacing": 5040, "changepoint_distance_end": 6, "constant": true} and transformations {"fillna": "quadratic", "transformations": {"0": "RollingMean100thN", "1": "RollingMeanTransformer"}, "transformation_params": {"0": {}, "1": {"fixed": true, "window": 12, "macro_micro": false, "center": false, "mean_type": "arithmetic"}}}
Model Number: 51 with model LastValueNaive in generation 0 of 1 with params {} and transformations {"fillna": "ffill", "transformations": {"0": "EWMAFilter", "1": "QuantileTransformer"}, "transformation_params": {"0": {"span": 12}, "1": {"output_distribution": "uniform", "n_quantiles": 1000}}}
Model Number: 52 with model SectionalMotif in generation 0 of 1 with params {"window": 7, "point_method": "weighted_mean", "distance_metric": "canberra", "include_differenced": true, "k": 10, "stride_size": 1, "regression_type": null, "comparison_transformation": {"fillna": "time", "transformations": {"0": "AlignLastValue", "1": "AlignLastDiff"}, "transformation_params": {"0": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false, "threshold": 3, "threshold_method": "mean", "mean_type": "geometric"}, "1": {"rows": null, "displacement_rows": 7, "quantile": 0.2, "decay_span": 2}}}, "combination_transformation": null} and transformations {"fillna": "ffill", "transformations": {"0": "AnomalyRemoval"}, "transformation_params": {"0": {"method": "mad", "method_params": {"distribution": "uniform", "alpha": 0.05}, "fillna": "mean", "transform_dict": {"fillna": null, "transformations": {"0": "EWMAFilter"}, "transformation_params": {"0": {"span": 7}}}, "isolated_only": false, "on_inverse": false}}}
Model Number: 53 with model BasicLinearModel in generation 0 of 1 with params {"datepart_method": "simple_binarized", "changepoint_spacing": 120, "changepoint_distance_end": 60, "regression_type": null, "lambda_": null, "trend_phi": null, "holiday_countries_used": true} and transformations {"fillna": "ffill", "transformations": {"0": "ClipOutliers", "1": "Detrend", "2": "AlignLastValue", "3": "AlignLastValue"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 3.5, "fillna": null}, "1": {"model": "GLS", "phi": 1, "window": null, "transform_dict": null}, "2": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false, "threshold": 10, "threshold_method": "max", "mean_type": "arithmetic"}, "3": {"rows": 1, "lag": 1, "method": "additive", "strength": 0.9, "first_value_only": false, "threshold": 10, "threshold_method": "max", "mean_type": "arithmetic"}}}
Template Eval Error: Traceback (most recent call last):
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 2230, in TemplateWizard
    df_forecast = model_forecast(
                  ^^^^^^^^^^^^^^^
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 1562, in model_forecast
    model = model.fit(df_train_low, future_regressor_train)
            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 895, in fit
    self.model = self.model.fit(
                 ^^^^^^^^^^^^^^^
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/models/basics.py", line 3795, in fit
    self.beta = np.linalg.pinv(X_values.T @ X_values) @ X_values.T @ Y_values
                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/numpy/linalg/_linalg.py", line 2281, in pinv
    u, s, vt = svd(a, full_matrices=False, hermitian=hermitian)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/numpy/linalg/_linalg.py", line 1862, in svd
    u, s, vh = gufunc(a, signature=signature)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/numpy/linalg/_linalg.py", line 172, in _raise_linalgerror_svd_nonconvergence
    raise LinAlgError("SVD did not converge")
numpy.linalg.LinAlgError: SVD did not converge
 in model 53 in generation 0: BasicLinearModel
Model Number: 54 with model SectionalMotif in generation 0 of 1 with params {"window": 15, "point_method": "mean", "distance_metric": "kulczynski1", "include_differenced": true, "k": 20, "stride_size": 1, "regression_type": null, "comparison_transformation": {"fillna": "rolling_mean", "transformations": {"0": "Detrend"}, "transformation_params": {"0": {"model": "GLS", "phi": 1, "window": 365, "transform_dict": {"fillna": null, "transformations": {"0": "AnomalyRemoval"}, "transformation_params": {"0": {"method": "zscore", "transform_dict": {"transformations": {"0": "DatepartRegression"}, "transformation_params": {"0": {"datepart_method": "simple_3", "regression_model": {"model": "ElasticNet", "model_params": {}}}}}, "method_params": {"distribution": "uniform", "alpha": 0.05}}}}}}}, "combination_transformation": null} and transformations {"fillna": "rolling_mean_24", "transformations": {"0": "SinTrend", "1": "DifferencedTransformer", "2": "StandardScaler", "3": "AnomalyRemoval", "4": "StandardScaler", "5": "AlignLastValue"}, "transformation_params": {"0": {}, "1": {"lag": 1, "fill": "bfill"}, "2": {}, "3": {"method": "mad", "method_params": {"distribution": "chi2", "alpha": 0.05}, "fillna": "rolling_mean_24", "transform_dict": {"fillna": "linear", "transformations": {"0": "StandardScaler", "1": "AlignLastValue"}, "transformation_params": {"0": {}, "1": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false, "threshold": 10, "threshold_method": "max", "mean_type": "arithmetic"}}}, "isolated_only": false, "on_inverse": false}, "4": {}, "5": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false, "threshold": 1, "threshold_method": "max", "mean_type": "arithmetic"}}}
Model Number: 55 with model GLS in generation 0 of 1 with params {"changepoint_spacing": 5040, "changepoint_distance_end": 28, "constant": true} and transformations {"fillna": "pchip", "transformations": {"0": "STLFilter", "1": "AlignLastValue", "2": "DifferencedTransformer", "3": "AlignLastValue"}, "transformation_params": {"0": {"decomp_type": "seasonal_decompose", "part": "trend"}, "1": {"rows": 1, "lag": 1, "method": "additive", "strength": 0.5, "first_value_only": false, "threshold": 10, "threshold_method": "mean", "mean_type": "arithmetic"}, "2": {"lag": 1, "fill": "bfill"}, "3": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false, "threshold": 1, "threshold_method": "max", "mean_type": "arithmetic"}}}
Model Number: 56 with model AverageValueNaive in generation 0 of 1 with params {"method": "Weighted_Mean", "window": 28} and transformations {"fillna": "zero", "transformations": {"0": "IntermittentOccurrence"}, "transformation_params": {"0": {"center": "mean"}}}
Model Number: 57 with model SeasonalityMotif in generation 0 of 1 with params {"window": 30, "point_method": "midhinge", "distance_metric": "chebyshev", "k": 3, "datepart_method": "anchored_warped_fourier:us_school", "independent": false} and transformations {"fillna": "rolling_mean", "transformations": {"0": "ClipOutliers", "1": "Detrend"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 1, "fillna": null}, "1": {"model": "GLS", "phi": 1, "window": null, "transform_dict": null}}}
Model Number: 58 with model SeasonalityMotif in generation 0 of 1 with params {"window": 5, "point_method": "trimmed_mean_40", "distance_metric": "canberra", "k": 1, "datepart_method": "simple", "independent": true} and transformations {"fillna": "ffill", "transformations": {"0": "RobustScaler", "1": "IntermittentOccurrence", "2": "AlignLastValue", "3": "AlignLastValue"}, "transformation_params": {"0": {}, "1": {"center": "mean"}, "2": {"rows": 4, "lag": 1, "method": "additive", "strength": 0.7, "first_value_only": false, "threshold": 10, "threshold_method": "mean", "mean_type": "arithmetic"}, "3": {"rows": 2, "lag": 1, "method": "additive", "strength": 0.2, "first_value_only": false, "threshold": 1, "threshold_method": "mean", "mean_type": "arithmetic"}}}
Template Eval Error: Traceback (most recent call last):
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 2230, in TemplateWizard
    df_forecast = model_forecast(
                  ^^^^^^^^^^^^^^^
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 1563, in model_forecast
    return model.predict(
           ^^^^^^^^^^^^^^
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 916, in predict
    raise ValueError(
ValueError: Model SeasonalityMotif returned NaN for one or more series. fail_on_forecast_nan=True
 in model 58 in generation 0: SeasonalityMotif
Model Number: 59 with model SectionalMotif in generation 0 of 1 with params {"window": 50, "point_method": "median", "distance_metric": "cosine", "include_differenced": true, "k": 15, "stride_size": 5, "regression_type": null, "comparison_transformation": null, "combination_transformation": null} and transformations {"fillna": "zero", "transformations": {"0": "MaxAbsScaler", "1": "FIRFilter", "2": "AlignLastValue", "3": "AlignLastValue", "4": "ClipOutliers", "5": "RobustScaler"}, "transformation_params": {"0": {}, "1": {"numtaps": 7, "cutoff_hz": 10, "window": ["kaiser", 4.0], "sampling_frequency": 6, "on_transform": true, "on_inverse": false, "bounds_only": false}, "2": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false, "threshold": null, "threshold_method": "mean", "mean_type": "arithmetic"}, "3": {"rows": 4, "lag": 84, "method": "additive", "strength": 1.0, "first_value_only": false, "threshold": 1, "threshold_method": "max", "mean_type": "arithmetic"}, "4": {"method": "clip", "std_threshold": 5, "fillna": null}, "5": {}}}
Template Eval Error: Traceback (most recent call last):
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 8295, in _fit
    df = self._fit_one(df, i)
         ^^^^^^^^^^^^^^^^^^^^
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 8279, in _fit_one
    df = self.transformers[i].fit_transform(df)
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 6448, in fit_transform
    return self.transform(df)
           ^^^^^^^^^^^^^^^^^^
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 6421, in transform
    return self.filter(df)
           ^^^^^^^^^^^^^^^
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 6403, in filter
    fft_fir_filter_to_timeseries(
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/fir_filter.py", line 112, in fft_fir_filter_to_timeseries
    fir_coefficients = firwin(numtaps=numtaps, cutoff=cutoff_norm, window=window)
                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/scipy/signal/_fir_filter_design.py", line 396, in firwin
    raise ValueError("Invalid cutoff frequency: frequencies must be "
ValueError: Invalid cutoff frequency: frequencies must be greater than 0 and less than fs/2.

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 2230, in TemplateWizard
    df_forecast = model_forecast(
                  ^^^^^^^^^^^^^^^
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 1562, in model_forecast
    model = model.fit(df_train_low, future_regressor_train)
            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 887, in fit
    df_train_transformed = self.transformer_object._fit(df)
                           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 8300, in _fit
    raise Exception(err_str) from e
Exception: Transformer FIRFilter failed on fit from params zero {'0': {}, '1': {'numtaps': 7, 'cutoff_hz': 10, 'window': ['kaiser', 4.0], 'sampling_frequency': 6, 'on_transform': True, 'on_inverse': False, 'bounds_only': False}, '2': {'rows': 1, 'lag': 1, 'method': 'additive', 'strength': 1.0, 'first_value_only': False, 'threshold': None, 'threshold_method': 'mean', 'mean_type': 'arithmetic'}, '3': {'rows': 4, 'lag': 84, 'method': 'additive', 'strength': 1.0, 'first_value_only': False, 'threshold': 1, 'threshold_method': 'max', 'mean_type': 'arithmetic'}, '4': {'method': 'clip', 'std_threshold': 5, 'fillna': None}, '5': {}} with error ValueError('Invalid cutoff frequency: frequencies must be greater than 0 and less than fs/2.')
 in model 59 in generation 0: SectionalMotif
Model Number: 60 with model ConstantNaive in generation 0 of 1 with params {"constant": 0} and transformations {"fillna": "ffill", "transformations": {"0": "CenterLastValue", "1": "MinMaxScaler", "2": "AlignLastValue"}, "transformation_params": {"0": {"rows": 1}, "1": {}, "2": {"rows": 1, "lag": 1, "method": "additive", "strength": 0.5, "first_value_only": false, "threshold": 10, "threshold_method": "mean", "mean_type": "arithmetic"}}}
New Generation: 1 of 1
Model Number: 61 with model BasicLinearModel in generation 1 of 1 with params {"datepart_method": "simple_binarized", "changepoint_spacing": 90, "changepoint_distance_end": 520, "regression_type": null, "lambda_": 10000, "trend_phi": 0.99, "holiday_countries_used": true} and transformations {"fillna": "time", "transformations": {"0": "AlignLastValue", "1": "IntermittentOccurrence", "2": "RobustScaler", "3": "AlignLastValue"}, "transformation_params": {"0": {"rows": 1, "lag": 2, "method": "multiplicative", "strength": 0.9, "first_value_only": false, "threshold": 1, "threshold_method": "mean"}, "1": {"center": "mean"}, "2": {}, "3": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false, "threshold": 1, "threshold_method": "max", "mean_type": "arithmetic"}}}
Template Eval Error: Traceback (most recent call last):
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 2230, in TemplateWizard
    df_forecast = model_forecast(
                  ^^^^^^^^^^^^^^^
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 1562, in model_forecast
    model = model.fit(df_train_low, future_regressor_train)
            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 895, in fit
    self.model = self.model.fit(
                 ^^^^^^^^^^^^^^^
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/models/basics.py", line 3789, in fit
    np.linalg.inv(X_values.T @ X_values + self.lambda_ * I)
ValueError: matmul: Input operand 1 has a mismatch in its core dimension 0, with gufunc signature (n?,k),(k,m?)->(n?,m?) (size 1159 is different from 2424)
 in model 61 in generation 1: BasicLinearModel
Model Number: 62 with model SeasonalNaive in generation 1 of 1 with params {"method": "lastvalue", "lag_1": 364, "lag_2": 28} and transformations {"fillna": "ffill", "transformations": {"0": "STLFilter", "1": "DatepartRegression"}, "transformation_params": {"0": {"decomp_type": "STL", "part": "trend", "seasonal": 7}, "1": {"regression_model": {"model": "ElasticNet", "model_params": {"l1_ratio": 0.5, "fit_intercept": true, "selection": "cyclic", "max_iter": 200}}, "datepart_method": ["dayofweek", [365.25, 4]], "polynomial_degree": null, "transform_dict": null, "holiday_countries_used": false, "lags": null, "forward_lags": null}}}
Model Number: 63 with model ConstantNaive in generation 1 of 1 with params {"constant": 0} and transformations {"fillna": "ffill_mean_biased", "transformations": {"0": "AlignLastValue", "1": "MeanPercentSplitter", "2": "AlignLastValue"}, "transformation_params": {"0": {"rows": 4, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false, "threshold": null, "threshold_method": "max", "mean_type": "arithmetic"}, "1": {"window": 3, "min_mean_threshold": 0.1}, "2": {"rows": 7, "lag": 168, "method": "additive", "strength": 1.0, "first_value_only": false, "threshold": null, "threshold_method": "max", "mean_type": "arithmetic"}}}
Model Number: 64 with model LastValueNaive in generation 1 of 1 with params {} and transformations {"fillna": "ffill", "transformations": {"0": "AlignLastValue", "1": "QuantileTransformer"}, "transformation_params": {"0": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false, "threshold": 1, "threshold_method": "max", "mean_type": "arithmetic"}, "1": {"output_distribution": "uniform", "n_quantiles": 1000}}}
Model Number: 65 with model SectionalMotif in generation 1 of 1 with params {"window": 7, "point_method": "weighted_mean", "distance_metric": "braycurtis", "include_differenced": true, "k": 5, "stride_size": 1, "regression_type": null, "comparison_transformation": {"fillna": "zero", "transformations": {"0": "StandardScaler"}, "transformation_params": {"0": {}}}, "combination_transformation": null} and transformations {"fillna": "rolling_mean_24", "transformations": {"0": "QuantileTransformer"}, "transformation_params": {"0": {"output_distribution": "uniform", "n_quantiles": 1000}}}
Model Number: 66 with model SectionalMotif in generation 1 of 1 with params {"window": 5, "point_method": "midhinge", "distance_metric": "mahalanobis", "include_differenced": true, "k": 5, "stride_size": 1, "regression_type": null, "comparison_transformation": null, "combination_transformation": null} and transformations {"fillna": "quadratic", "transformations": {"0": "ClipOutliers", "1": "AlignLastValue"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 3.5, "fillna": null}, "1": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false, "threshold": 1, "threshold_method": "max", "mean_type": "arithmetic"}}}
Model Number: 67 with model LastValueNaive in generation 1 of 1 with params {} and transformations {"fillna": "ffill", "transformations": {"0": "AlignLastValue", "1": "Detrend", "2": "LevelShiftTransformer", "3": "AlignLastValue"}, "transformation_params": {"0": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false, "threshold": 1, "threshold_method": "max", "mean_type": "arithmetic"}, "1": {"model": "GLS", "phi": 0.999, "window": null, "transform_dict": null}, "2": {"window_size": 90, "alpha": 2.5, "grouping_forward_limit": 5, "max_level_shifts": 5, "alignment": "last_value", "output": "univariate", "remove_at_shift": false, "shift_remove_window": 1, "shift_fillna": "linear", "window_method": "diff_overlap"}, "3": {"rows": 1, "lag": 7, "method": "additive", "strength": 1.0, "first_value_only": false, "threshold": 1, "threshold_method": "max", "mean_type": "arithmetic"}}}
Model Number: 68 with model SectionalMotif in generation 1 of 1 with params {"window": 15, "point_method": "mean", "distance_metric": "kulczynski1", "include_differenced": true, "k": 20, "stride_size": 1, "regression_type": null, "comparison_transformation": {"fillna": "rolling_mean", "transformations": {"0": "Detrend"}, "transformation_params": {"0": {"model": "GLS", "phi": 1, "window": 365, "transform_dict": {"fillna": null, "transformations": {"0": "AnomalyRemoval"}, "transformation_params": {"0": {"method": "zscore", "transform_dict": {"transformations": {"0": "DatepartRegression"}, "transformation_params": {"0": {"datepart_method": "simple_3", "regression_model": {"model": "ElasticNet", "model_params": {}}}}}, "method_params": {"distribution": "uniform", "alpha": 0.05}}}}}}}, "combination_transformation": null} and transformations {"fillna": "linear", "transformations": {"0": "RollingMeanTransformer", "1": "DifferencedTransformer", "2": "AlignLastValue", "3": "AnomalyRemoval", "4": "AlignLastValue"}, "transformation_params": {"0": {"fixed": true, "window": 12, "macro_micro": false, "center": false, "mean_type": "arithmetic"}, "1": {"lag": 1, "fill": "bfill"}, "2": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false, "threshold": null, "threshold_method": "mean", "mean_type": "arithmetic"}, "3": {"method": "mad", "method_params": {"distribution": "chi2", "alpha": 0.05}, "fillna": "rolling_mean_24", "transform_dict": {"fillna": "linear", "transformations": {"0": "StandardScaler", "1": "AlignLastValue"}, "transformation_params": {"0": {}, "1": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false, "threshold": 10, "threshold_method": "max", "mean_type": "arithmetic"}}}, "isolated_only": false, "on_inverse": false}, "4": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false, "threshold": 1, "threshold_method": "max", "mean_type": "arithmetic"}}}
Model Number: 69 with model SectionalMotif in generation 1 of 1 with params {"window": 5, "point_method": "weighted_mean", "distance_metric": "braycurtis", "include_differenced": true, "k": 10, "stride_size": 1, "regression_type": null, "comparison_transformation": {"fillna": "zero", "transformations": {"0": "StandardScaler"}, "transformation_params": {"0": {}}}, "combination_transformation": null} and transformations {"fillna": "rolling_mean_24", "transformations": {"0": "ClipOutliers", "1": "QuantileTransformer", "2": "RobustScaler"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 2, "fillna": null}, "1": {"output_distribution": "uniform", "n_quantiles": 1000}, "2": {}}}
Model Number: 70 with model BasicLinearModel in generation 1 of 1 with params {"datepart_method": "common_fourier", "changepoint_spacing": 5040, "changepoint_distance_end": 60, "regression_type": null, "lambda_": null, "trend_phi": null, "holiday_countries_used": false} and transformations {"fillna": "zero", "transformations": {"0": "ClipOutliers", "1": "LevelShiftTransformer", "2": "UpscaleDownscaleTransformer", "3": "AlignLastValue"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 2, "fillna": null}, "1": {"window_size": 90, "alpha": 3.0, "grouping_forward_limit": 5, "max_level_shifts": 30, "alignment": "last_value", "output": "multivariate", "remove_at_shift": false, "shift_remove_window": 1, "shift_fillna": "cubic", "window_method": "exclusive"}, "2": {"mode": "upscale", "factor": 1, "down_method": "mean", "fill_method": "ffill"}, "3": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false, "threshold": 1, "threshold_method": "max", "mean_type": "arithmetic"}}}
Template Eval Error: Traceback (most recent call last):
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 2230, in TemplateWizard
    df_forecast = model_forecast(
                  ^^^^^^^^^^^^^^^
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 1563, in model_forecast
    return model.predict(
           ^^^^^^^^^^^^^^
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 908, in predict
    df_forecast = self.model.predict(
                  ^^^^^^^^^^^^^^^^^^^
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/models/basics.py", line 3858, in predict
    X_values @ self.beta, columns=self.column_names, index=test_index
    ~~~~~~~~~^~~~~~~~~~~
ValueError: matmul: Input operand 1 has a mismatch in its core dimension 0, with gufunc signature (n?,k),(k,m?)->(n?,m?) (size 55 is different from 39)
 in model 70 in generation 1: BasicLinearModel
Model Number: 71 with model SeasonalityMotif in generation 1 of 1 with params {"window": 10, "point_method": "weighted_mean", "distance_metric": "mae", "k": 1, "datepart_method": "anchored_warped_fourier:us_school", "independent": false} and transformations {"fillna": "rolling_mean", "transformations": {"0": "Constraint", "1": "Detrend", "2": "UpscaleDownscaleTransformer"}, "transformation_params": {"0": {"constraint_method": "historic_diff", "constraint_direction": "lower", "constraint_regularization": 1.0, "constraint_value": 0.2, "bounds_only": false, "fillna": null}, "1": {"model": "Linear", "phi": 1, "window": 90, "transform_dict": null}, "2": {"mode": "upscale", "factor": 1, "down_method": "mean", "fill_method": "ffill"}}}
Model Number: 72 with model LastValueNaive in generation 1 of 1 with params {} and transformations {"fillna": "rolling_mean_24", "transformations": {"0": "AlignLastValue", "1": "QuantileTransformer"}, "transformation_params": {"0": {"rows": 1, "lag": 84, "method": "multiplicative", "strength": 1.0, "first_value_only": false, "threshold": 10, "threshold_method": "max", "mean_type": "arithmetic"}, "1": {"output_distribution": "uniform", "n_quantiles": 1000}}}
Model Number: 73 with model LastValueNaive in generation 1 of 1 with params {} and transformations {"fillna": "ffill", "transformations": {"0": "PositiveShift", "1": "SinTrend", "2": "UpscaleDownscaleTransformer"}, "transformation_params": {"0": {}, "1": {}, "2": {"mode": "upscale", "factor": 1, "down_method": "mean", "fill_method": "ffill"}}}
Model Number: 74 with model AverageValueNaive in generation 1 of 1 with params {"method": "Median", "window": 28} and transformations {"fillna": "ffill_mean_biased", "transformations": {"0": "Round", "1": "DifferencedTransformer", "2": "StandardScaler"}, "transformation_params": {"0": {"decimals": 1, "on_transform": true, "on_inverse": false}, "1": {"lag": 1, "fill": "bfill"}, "2": {}}}
Model Number: 75 with model BasicLinearModel in generation 1 of 1 with params {"datepart_method": "common_fourier", "changepoint_spacing": 28, "changepoint_distance_end": 5040, "regression_type": null, "lambda_": 1, "trend_phi": null, "holiday_countries_used": false} and transformations {"fillna": "zero", "transformations": {"0": "FIRFilter", "1": "SeasonalDifference", "2": "G711Scaler", "3": "AlignLastValue", "4": "cffilter"}, "transformation_params": {"0": {"numtaps": 128, "cutoff_hz": 100, "window": ["kaiser", 4.0], "sampling_frequency": 4, "on_transform": true, "on_inverse": false, "bounds_only": false}, "1": {"lag_1": 7, "method": "Mean"}, "2": {"mode": "a", "mu": 100.0, "A": 50.0, "center": "mean", "scale_method": "std", "scale_factor": 2.894632885325273, "min_scale": 1e-06, "clip": true, "zero_offset": 0.0, "fill_method": "interpolate", "on_transform": true, "on_inverse": true, "bounds_only": false}, "3": {"rows": 1, "lag": 1, "method": "additive", "strength": 0.5, "first_value_only": false, "threshold": null, "threshold_method": "max", "mean_type": "arithmetic"}, "4": {}}}
Template Eval Error: Traceback (most recent call last):
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 8295, in _fit
    df = self._fit_one(df, i)
         ^^^^^^^^^^^^^^^^^^^^
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 8279, in _fit_one
    df = self.transformers[i].fit_transform(df)
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 6448, in fit_transform
    return self.transform(df)
           ^^^^^^^^^^^^^^^^^^
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 6421, in transform
    return self.filter(df)
           ^^^^^^^^^^^^^^^
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 6403, in filter
    fft_fir_filter_to_timeseries(
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/fir_filter.py", line 112, in fft_fir_filter_to_timeseries
    fir_coefficients = firwin(numtaps=numtaps, cutoff=cutoff_norm, window=window)
                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/scipy/signal/_fir_filter_design.py", line 396, in firwin
    raise ValueError("Invalid cutoff frequency: frequencies must be "
ValueError: Invalid cutoff frequency: frequencies must be greater than 0 and less than fs/2.

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 2230, in TemplateWizard
    df_forecast = model_forecast(
                  ^^^^^^^^^^^^^^^
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 1562, in model_forecast
    model = model.fit(df_train_low, future_regressor_train)
            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 887, in fit
    df_train_transformed = self.transformer_object._fit(df)
                           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 8300, in _fit
    raise Exception(err_str) from e
Exception: Transformer FIRFilter failed on fit from params zero {'0': {'numtaps': 128, 'cutoff_hz': 100, 'window': ['kaiser', 4.0], 'sampling_frequency': 4, 'on_transform': True, 'on_inverse': False, 'bounds_only': False}, '1': {'lag_1': 7, 'method': 'Mean'}, '2': {'mode': 'a', 'mu': 100.0, 'A': 50.0, 'center': 'mean', 'scale_method': 'std', 'scale_factor': 2.894632885325273, 'min_scale': 1e-06, 'clip': True, 'zero_offset': 0.0, 'fill_method': 'interpolate', 'on_transform': True, 'on_inverse': True, 'bounds_only': False}, '3': {'rows': 1, 'lag': 1, 'method': 'additive', 'strength': 0.5, 'first_value_only': False, 'threshold': None, 'threshold_method': 'max', 'mean_type': 'arithmetic'}, '4': {}} with error ValueError('Invalid cutoff frequency: frequencies must be greater than 0 and less than fs/2.')
 in model 75 in generation 1: BasicLinearModel
Model Number: 76 with model ConstantNaive in generation 1 of 1 with params {"constant": 0} and transformations {"fillna": "ffill", "transformations": {"0": "G726Filter", "1": "AlignLastValue", "2": "BKBandpassFilter", "3": "CumSumTransformer", "4": "DatepartRegression"}, "transformation_params": {"0": {"quant_bits": 4, "adaptation_rate": 0.9641121887264618, "prediction_alpha": 0.9437317406672192, "floor_step": 0.03272677759007476, "dynamic_range": 1.244898525944512, "blend": 0.16137863511954614, "noise_gate": 0.020784410369082476, "fill_method": "interpolate", "on_transform": true, "on_inverse": false, "quantizer": "uniform", "use_adaptive_predictor": true, "predictor_leak": 0.9996500337746135, "bounds_only": false}, "1": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false, "threshold": null, "threshold_method": "max", "mean_type": "arithmetic"}, "2": {"low": 28, "high": 32, "K": 1, "lanczos_factor": false, "return_diff": false, "on_transform": true, "on_inverse": false}, "3": {}, "4": {"regression_model": {"model": "ElasticNet", "model_params": {"l1_ratio": 0.5, "fit_intercept": false, "selection": "cyclic", "max_iter": 2000}}, "datepart_method": "expanded", "polynomial_degree": null, "transform_dict": null, "holiday_countries_used": false, "lags": 2, "forward_lags": 2}}}
Model Number: 77 with model GLS in generation 1 of 1 with params {"changepoint_spacing": 60, "changepoint_distance_end": null, "constant": true} and transformations {"fillna": "ffill", "transformations": {"0": "ClipOutliers", "1": "QuantileTransformer", "2": "Round"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 3.5, "fillna": null}, "1": {"output_distribution": "uniform", "n_quantiles": 1000}, "2": {"model": "middle", "decimals": 2, "on_transform": true, "on_inverse": true}}}
Model Number: 78 with model SectionalMotif in generation 1 of 1 with params {"window": 7, "point_method": "midhinge", "distance_metric": "russellrao", "include_differenced": true, "k": 5, "stride_size": 1, "regression_type": null, "comparison_transformation": null, "combination_transformation": null} and transformations {"fillna": "ffill", "transformations": {"0": "BKBandpassFilter", "1": "AlignLastValue", "2": "HistoricValues"}, "transformation_params": {"0": {"low": 4, "high": 32, "K": 6, "lanczos_factor": false, "return_diff": true, "on_transform": true, "on_inverse": false}, "1": {"rows": 1, "lag": 2, "method": "additive", "strength": 1.0, "first_value_only": false}, "2": {"window": null}}}
Model Number: 79 with model LastValueNaive in generation 1 of 1 with params {} and transformations {"fillna": "rolling_mean_24", "transformations": {"0": "RobustScaler", "1": "SinTrend", "2": "AnomalyRemoval"}, "transformation_params": {"0": {}, "1": {}, "2": {"method": "rolling_zscore", "method_params": {"distribution": "uniform", "alpha": 0.05, "rolling_periods": 90, "center": false}, "fillna": "ffill", "transform_dict": {"fillna": "zero", "transformations": {"0": "STLFilter"}, "transformation_params": {"0": {"decomp_type": "STL", "part": "trend", "seasonal": 7}}}, "isolated_only": false, "on_inverse": false}}}
Model Number: 80 with model LastValueNaive in generation 1 of 1 with params {} and transformations {"fillna": "ffill", "transformations": {"0": "ClipOutliers", "1": "QuantileTransformer"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 2, "fillna": null}, "1": {"output_distribution": "uniform", "n_quantiles": 1000}}}
Model Number: 81 with model SeasonalNaive in generation 1 of 1 with params {"method": "lastvalue", "lag_1": 28, "lag_2": 28} and transformations {"fillna": "ffill", "transformations": {"0": "ThetaTransformer", "1": "MaxAbsScaler", "2": "Round"}, "transformation_params": {"0": {"theta_values": [0.4, 1.6]}, "1": {}, "2": {"decimals": 0, "on_transform": false, "on_inverse": true}}}
Model Number: 82 with model ConstantNaive in generation 1 of 1 with params {"constant": 0} and transformations {"fillna": "rolling_mean_24", "transformations": {"0": "AlignLastValue", "1": "MeanPercentSplitter", "2": "AlignLastValue"}, "transformation_params": {"0": {"rows": 1, "lag": 7, "method": "multiplicative", "strength": 1.0, "first_value_only": false, "threshold": 10, "threshold_method": "mean", "mean_type": "geometric"}, "1": {"window": 3, "min_mean_threshold": 0.1}, "2": {"rows": 7, "lag": 168, "method": "additive", "strength": 1.0, "first_value_only": false, "threshold": null, "threshold_method": "max", "mean_type": "arithmetic"}}}
Model Number: 83 with model ConstantNaive in generation 1 of 1 with params {"constant": 1} and transformations {"fillna": "akima", "transformations": {"0": "HistoricValues", "1": "BKBandpassFilter", "2": "QuantileTransformer"}, "transformation_params": {"0": {"window": 730}, "1": {"low": 7, "high": 32, "K": 3, "lanczos_factor": true, "return_diff": true, "on_transform": true, "on_inverse": false}, "2": {"output_distribution": "normal", "n_quantiles": 1000}}}
Model Number: 84 with model GLS in generation 1 of 1 with params {"changepoint_spacing": 5040, "changepoint_distance_end": 6, "constant": true} and transformations {"fillna": "akima", "transformations": {"0": "RollingMean100thN"}, "transformation_params": {"0": {}}}
Model Number: 85 with model SectionalMotif in generation 1 of 1 with params {"window": 7, "point_method": "weighted_mean", "distance_metric": "rogerstanimoto", "include_differenced": true, "k": 5, "stride_size": 1, "regression_type": null, "comparison_transformation": {"fillna": "ffill", "transformations": {"0": "AlignLastDiff"}, "transformation_params": {"0": {"rows": 1, "displacement_rows": 7, "quantile": 1.0, "decay_span": null}}}, "combination_transformation": null} and transformations {"fillna": "mean", "transformations": {"0": "ClipOutliers", "1": "Detrend", "2": "UpscaleDownscaleTransformer", "3": "AlignLastValue"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 2, "fillna": null}, "1": {"model": "Linear", "phi": 1, "window": 90, "transform_dict": null}, "2": {"mode": "upscale", "factor": 1, "down_method": "mean", "fill_method": "ffill"}, "3": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false, "threshold": 1, "threshold_method": "max", "mean_type": "arithmetic"}}}
Model Number: 86 with model SeasonalNaive in generation 1 of 1 with params {"method": "mean", "lag_1": 7, "lag_2": 96} and transformations {"fillna": "rolling_mean", "transformations": {"0": "ThetaTransformer", "1": "Detrend", "2": "bkfilter", "3": "AlignLastValue"}, "transformation_params": {"0": {"theta_values": [0.4, 1.6]}, "1": {"model": "Linear", "phi": 1, "window": 90, "transform_dict": null}, "2": {}, "3": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false, "threshold": 1, "threshold_method": "max", "mean_type": "arithmetic"}}}
Model Number: 87 with model SectionalMotif in generation 1 of 1 with params {"window": 3, "point_method": "weighted_mean", "distance_metric": "canberra", "include_differenced": true, "k": 10, "stride_size": 1, "regression_type": null, "comparison_transformation": null, "combination_transformation": null} and transformations {"fillna": "mean", "transformations": {"0": "QuantileTransformer", "1": "QuantileTransformer"}, "transformation_params": {"0": {"output_distribution": "uniform", "n_quantiles": 1000}, "1": {"output_distribution": "uniform", "n_quantiles": 1000}}}
Model Number: 88 with model LastValueNaive in generation 1 of 1 with params {} and transformations {"fillna": "ffill", "transformations": {"0": "ClipOutliers", "1": "StandardScaler", "2": "UpscaleDownscaleTransformer"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 2, "fillna": null}, "1": {}, "2": {"mode": "upscale", "factor": 1, "down_method": "mean", "fill_method": "ffill"}}}
Model Number: 89 with model SeasonalNaive in generation 1 of 1 with params {"method": "lastvalue", "lag_1": 96, "lag_2": 1} and transformations {"fillna": "rolling_mean_24", "transformations": {"0": "ClipOutliers", "1": "QuantileTransformer", "2": "HistoricValues", "3": "AlignLastValue"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 5, "fillna": null}, "1": {"output_distribution": "uniform", "n_quantiles": 1000}, "2": {"window": 364}, "3": {"rows": 1, "lag": 7, "method": "additive", "strength": 1.0, "first_value_only": false, "threshold": 1, "threshold_method": "max", "mean_type": "arithmetic"}}}
Model Number: 90 with model ConstantNaive in generation 1 of 1 with params {"constant": 1} and transformations {"fillna": "ffill", "transformations": {"0": "ClipOutliers", "1": "AlignLastValue", "2": "BKBandpassFilter", "3": "CumSumTransformer", "4": "DatepartRegression", "5": "STLFilter"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 2, "fillna": null}, "1": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false, "threshold": null, "threshold_method": "max", "mean_type": "arithmetic"}, "2": {"low": 28, "high": 32, "K": 1, "lanczos_factor": false, "return_diff": false, "on_transform": true, "on_inverse": false}, "3": {}, "4": {"regression_model": {"model": "ElasticNet", "model_params": {"l1_ratio": 0.5, "fit_intercept": false, "selection": "cyclic", "max_iter": 2000}}, "datepart_method": "expanded", "polynomial_degree": null, "transform_dict": null, "holiday_countries_used": false, "lags": 2, "forward_lags": 2}, "5": {"decomp_type": "STL", "part": "trend", "seasonal": 7}}}
Model Number: 91 with model ConstantNaive in generation 1 of 1 with params {"constant": 1} and transformations {"fillna": "ffill_mean_biased", "transformations": {"0": "G726Filter", "1": "MeanPercentSplitter", "2": "QuantileTransformer"}, "transformation_params": {"0": {"quant_bits": 4, "adaptation_rate": 0.9641121887264618, "prediction_alpha": 0.9437317406672192, "floor_step": 0.03272677759007476, "dynamic_range": 1.244898525944512, "blend": 0.16137863511954614, "noise_gate": 0.020784410369082476, "fill_method": "interpolate", "on_transform": true, "on_inverse": false, "quantizer": "uniform", "use_adaptive_predictor": true, "predictor_leak": 0.9996500337746135, "bounds_only": false}, "1": {"window": 3, "min_mean_threshold": 0.1}, "2": {"output_distribution": "normal", "n_quantiles": 1000}}}
Model Number: 92 with model SeasonalNaive in generation 1 of 1 with params {"method": "lastvalue", "lag_1": 8, "lag_2": 28} and transformations {"fillna": "seasonal_linear_window_3", "transformations": {"0": "LevelShiftTransformer", "1": "Round", "2": "ReconciliationTransformer", "3": "AlignLastValue"}, "transformation_params": {"0": {"window_size": 90, "alpha": 2.0, "grouping_forward_limit": 4, "max_level_shifts": 30, "alignment": "average", "output": "multivariate", "remove_at_shift": true, "shift_remove_window": 2, "shift_fillna": "linear", "window_method": "overlap"}, "1": {"decimals": 0, "on_transform": true, "on_inverse": false}, "2": {"group_size": 30, "hierarchy_map": null, "reconciliation_params": {"method": "none", "cov_source": "historical", "weighting": "full", "shrinkage": 0.7, "ledoit_wolf": true, "ridge": null, "volatility_params": {"method": "std", "power": 0.5810096015115083, "mix": 0.35498029443727297}, "iterative_params": {"max_iterations": 15, "convergence_threshold": 1e-06, "damping_factor": 0.6376414411474407}}}, "3": {"rows": 1, "lag": 7, "method": "additive", "strength": 1.0, "first_value_only": false, "threshold": 1, "threshold_method": "max", "mean_type": "arithmetic"}}}
Model Number: 93 with model AverageValueNaive in generation 1 of 1 with params {"method": "Mean", "window": null} and transformations {"fillna": "mean", "transformations": {"0": "ClipOutliers", "1": "ClipOutliers", "2": "DifferencedTransformer", "3": "ReplaceConstant"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 3, "fillna": null}, "1": {"method": "clip", "std_threshold": 3.5, "fillna": null}, "2": {}, "3": {"constant": 0, "reintroduction_model": {"model": "SGD", "model_params": {"learning_rate": 0.15, "max_iter": 6, "l2": 0.0001, "column_batch_size": 256, "probability_threshold": 0.5}, "vectorized": true, "datepart_method": "simple_poly"}, "fillna": "pchip"}}}
Model Number: 94 with model BasicLinearModel in generation 1 of 1 with params {"datepart_method": "recurring", "changepoint_spacing": 28, "changepoint_distance_end": 90, "regression_type": null, "lambda_": 0.01, "trend_phi": 0.98, "holiday_countries_used": true} and transformations {"fillna": "ffill", "transformations": {"0": "ClipOutliers", "1": "ClipOutliers", "2": "SeasonalDifference", "3": "AlignLastValue"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 2, "fillna": null}, "1": {"method": "clip", "std_threshold": 3, "fillna": null}, "2": {"lag_1": 12, "method": 5}, "3": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false, "threshold": 1, "threshold_method": "max", "mean_type": "arithmetic"}}}
Model Number: 95 with model SeasonalityMotif in generation 1 of 1 with params {"window": 15, "point_method": "closest", "distance_metric": "minkowski", "k": 1, "datepart_method": ["weekdaymonthofyear", "quarter", "dayofweek"], "independent": true} and transformations {"fillna": "ffill", "transformations": {"0": "Constraint", "1": "AlignLastValue"}, "transformation_params": {"0": {"constraint_method": "historic_diff", "constraint_direction": "lower", "constraint_regularization": 1.0, "constraint_value": 0.2, "bounds_only": false, "fillna": null}, "1": {"rows": 1, "lag": 1, "method": "additive", "strength": 0.2, "first_value_only": false, "threshold": null, "threshold_method": "max"}}}
Model Number: 96 with model SectionalMotif in generation 1 of 1 with params {"window": 15, "point_method": "weighted_mean", "distance_metric": "canberra", "include_differenced": true, "k": 5, "stride_size": 1, "regression_type": null, "comparison_transformation": {"fillna": "ffill", "transformations": {"0": "AlignLastDiff"}, "transformation_params": {"0": {"rows": 1, "displacement_rows": 7, "quantile": 1.0, "decay_span": null}}}, "combination_transformation": null} and transformations {"fillna": "ffill", "transformations": {"0": "SinTrend", "1": "Detrend", "2": "StandardScaler", "3": "AlignLastValue", "4": "StandardScaler", "5": "AlignLastValue"}, "transformation_params": {"0": {}, "1": {"model": "Linear", "phi": 1, "window": 90, "transform_dict": null}, "2": {}, "3": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false, "threshold": 1, "threshold_method": "max", "mean_type": "arithmetic"}, "4": {}, "5": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false, "threshold": 1, "threshold_method": "max", "mean_type": "arithmetic"}}}
Model Number: 97 with model SeasonalityMotif in generation 1 of 1 with params {"window": 10, "point_method": "median", "distance_metric": "mae", "k": 1, "datepart_method": "simple", "independent": true} and transformations {"fillna": "ffill", "transformations": {"0": "ClipOutliers", "1": "Discretize", "2": "UpscaleDownscaleTransformer"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 2, "fillna": null}, "1": {"discretization": "center", "n_bins": 10}, "2": {"mode": "upscale", "factor": 1, "down_method": "mean", "fill_method": "ffill"}}}
Model Number: 98 with model SeasonalityMotif in generation 1 of 1 with params {"window": 5, "point_method": "trimmed_mean_40", "distance_metric": "mae", "k": 5, "datepart_method": ["simple_binarized_poly"], "independent": false} and transformations {"fillna": "ffill", "transformations": {"0": "FFTFilter"}, "transformation_params": {"0": {"cutoff": 0.01, "reverse": false, "on_transform": true, "on_inverse": true}}}
Model Number: 99 with model ConstantNaive in generation 1 of 1 with params {"constant": 1} and transformations {"fillna": "ffill", "transformations": {"0": "EWMAFilter", "1": "PCA"}, "transformation_params": {"0": {"span": 3}, "1": {"whiten": true, "n_components": 24}}}
Template Eval Error: Traceback (most recent call last):
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 8295, in _fit
    df = self._fit_one(df, i)
         ^^^^^^^^^^^^^^^^^^^^
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 8279, in _fit_one
    df = self.transformers[i].fit_transform(df)
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 2655, in fit_transform
    return self._fit(df)
           ^^^^^^^^^^^^^
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 2595, in _fit
    return_df = self.transformer.fit_transform(df)
                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/sklearn/utils/_set_output.py", line 316, in wrapped
    data_to_wrap = f(self, X, *args, **kwargs)
                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/sklearn/base.py", line 1336, in wrapper
    return fit_method(estimator, *args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/sklearn/decomposition/_pca.py", line 466, in fit_transform
    U, S, _, X, x_is_centered, xp = self._fit(X)
                                    ^^^^^^^^^^^^
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/sklearn/decomposition/_pca.py", line 540, in _fit
    return self._fit_full(X, n_components, xp, is_array_api_compliant)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/sklearn/decomposition/_pca.py", line 554, in _fit_full
    raise ValueError(
ValueError: n_components=24 must be between 0 and min(n_samples, n_features)=10 with svd_solver='covariance_eigh'

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 2230, in TemplateWizard
    df_forecast = model_forecast(
                  ^^^^^^^^^^^^^^^
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 1562, in model_forecast
    model = model.fit(df_train_low, future_regressor_train)
            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 887, in fit
    df_train_transformed = self.transformer_object._fit(df)
                           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 8300, in _fit
    raise Exception(err_str) from e
Exception: Transformer PCA failed on fit from params ffill {'0': {'span': 3}, '1': {'whiten': True, 'n_components': 24}} with error ValueError("n_components=24 must be between 0 and min(n_samples, n_features)=10 with svd_solver='covariance_eigh'")
 in model 99 in generation 1: ConstantNaive
Model Number: 100 with model SectionalMotif in generation 1 of 1 with params {"window": 5, "point_method": "midhinge", "distance_metric": "canberra", "include_differenced": true, "k": 10, "stride_size": 2, "regression_type": null, "comparison_transformation": null, "combination_transformation": null} and transformations {"fillna": "ffill", "transformations": {"0": "RollingMeanTransformer", "1": "Detrend", "2": "UpscaleDownscaleTransformer", "3": "AlignLastValue"}, "transformation_params": {"0": {"fixed": true, "window": 12, "macro_micro": false, "center": false, "mean_type": "arithmetic"}, "1": {"model": "Linear", "phi": 1, "window": 90, "transform_dict": null}, "2": {"mode": "upscale", "factor": 1, "down_method": "mean", "fill_method": "ffill"}, "3": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false, "threshold": 1, "threshold_method": "max", "mean_type": "arithmetic"}}}
Validation Round: 1
Validation train index is DatetimeIndex(['2020-02-05', '2020-02-06', '2020-02-07', '2020-02-08',
               '2020-02-09', '2020-02-10', '2020-02-11', '2020-02-12',
               '2020-02-13', '2020-02-14',
               ...
               '2023-02-28', '2023-03-01', '2023-03-02', '2023-03-03',
               '2023-03-04', '2023-03-05', '2023-03-06', '2023-03-07',
               '2023-03-08', '2023-03-09'],
              dtype='datetime64[ns]', length=1129, freq=None)
Model Number: 1 of 15 with model SectionalMotif for Validation 1 with params {"window": 5, "point_method": "midhinge", "distance_metric": "mahalanobis", "include_differenced": true, "k": 5, "stride_size": 1, "regression_type": null, "comparison_transformation": null, "combination_transformation": null} and transformations {"fillna": "quadratic", "transformations": {"0": "ClipOutliers", "1": "AlignLastValue"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 3.5, "fillna": null}, "1": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false, "threshold": 1, "threshold_method": "max", "mean_type": "arithmetic"}}}
📈 1 - SectionalMotif with avg smape 120.31 in 0.02s:
Model Number: 2 of 15 with model SectionalMotif for Validation 1 with params {"window": 7, "point_method": "midhinge", "distance_metric": "russellrao", "include_differenced": true, "k": 5, "stride_size": 1, "regression_type": null, "comparison_transformation": null, "combination_transformation": null} and transformations {"fillna": "ffill", "transformations": {"0": "BKBandpassFilter", "1": "AlignLastValue", "2": "HistoricValues"}, "transformation_params": {"0": {"low": 4, "high": 32, "K": 6, "lanczos_factor": false, "return_diff": true, "on_transform": true, "on_inverse": false}, "1": {"rows": 1, "lag": 2, "method": "additive", "strength": 1.0, "first_value_only": false}, "2": {"window": null}}}
2 - SectionalMotif with avg smape 120.6 in 0.01s:
Model Number: 3 of 15 with model SeasonalityMotif for Validation 1 with params {"window": 10, "point_method": "closest", "distance_metric": "mae", "k": 1, "datepart_method": ["weekdaymonthofyear", "quarter", "dayofweek"], "independent": false} and transformations {"fillna": "ffill", "transformations": {"0": "ClipOutliers", "1": "Detrend", "2": "UpscaleDownscaleTransformer", "3": "AlignLastValue"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 2, "fillna": null}, "1": {"model": "Linear", "phi": 1, "window": 90, "transform_dict": null}, "2": {"mode": "upscale", "factor": 1, "down_method": "mean", "fill_method": "ffill"}, "3": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false, "threshold": 1, "threshold_method": "max", "mean_type": "arithmetic"}}}
3 - SeasonalityMotif with avg smape 131.63 in 0.07s:
Model Number: 4 of 15 with model SectionalMotif for Validation 1 with params {"window": 5, "point_method": "midhinge", "distance_metric": "canberra", "include_differenced": true, "k": 10, "stride_size": 2, "regression_type": null, "comparison_transformation": null, "combination_transformation": null} and transformations {"fillna": "ffill", "transformations": {"0": "RollingMeanTransformer", "1": "Detrend", "2": "UpscaleDownscaleTransformer", "3": "AlignLastValue"}, "transformation_params": {"0": {"fixed": true, "window": 12, "macro_micro": false, "center": false, "mean_type": "arithmetic"}, "1": {"model": "Linear", "phi": 1, "window": 90, "transform_dict": null}, "2": {"mode": "upscale", "factor": 1, "down_method": "mean", "fill_method": "ffill"}, "3": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false, "threshold": 1, "threshold_method": "max", "mean_type": "arithmetic"}}}
4 - SectionalMotif with avg smape 127.98 in 0.02s:
Model Number: 5 of 15 with model BasicLinearModel for Validation 1 with params {"datepart_method": "recurring", "changepoint_spacing": 28, "changepoint_distance_end": 90, "regression_type": null, "lambda_": 0.01, "trend_phi": 0.98, "holiday_countries_used": true} and transformations {"fillna": "ffill", "transformations": {"0": "Slice", "1": "ClipOutliers", "2": "SeasonalDifference", "3": "LevelShiftTransformer", "4": "LevelShiftTransformer", "5": "PositiveShift"}, "transformation_params": {"0": {"method": 0.2}, "1": {"method": "clip", "std_threshold": 3, "fillna": null}, "2": {"lag_1": 12, "method": 5}, "3": {"window_size": 7, "alpha": 2.0, "grouping_forward_limit": 5, "max_level_shifts": 30, "alignment": "rolling_diff_3nn"}, "4": {"window_size": 7, "alpha": 2.0, "grouping_forward_limit": 5, "max_level_shifts": 30, "alignment": "rolling_diff_3nn"}, "5": {}}}
5 - BasicLinearModel with avg smape 123.89 in 0.16s:
Model Number: 6 of 15 with model LastValueNaive for Validation 1 with params {} and transformations {"fillna": "ffill", "transformations": {"0": "AlignLastValue", "1": "Detrend", "2": "LevelShiftTransformer", "3": "AlignLastValue"}, "transformation_params": {"0": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false, "threshold": 1, "threshold_method": "max", "mean_type": "arithmetic"}, "1": {"model": "GLS", "phi": 0.999, "window": null, "transform_dict": null}, "2": {"window_size": 90, "alpha": 2.5, "grouping_forward_limit": 5, "max_level_shifts": 5, "alignment": "last_value", "output": "univariate", "remove_at_shift": false, "shift_remove_window": 1, "shift_fillna": "linear", "window_method": "diff_overlap"}, "3": {"rows": 1, "lag": 7, "method": "additive", "strength": 1.0, "first_value_only": false, "threshold": 1, "threshold_method": "max", "mean_type": "arithmetic"}}}
6 - LastValueNaive with avg smape 124.11 in 0.05s:
Model Number: 7 of 15 with model LastValueNaive for Validation 1 with params {} and transformations {"fillna": "ffill", "transformations": {"0": "AlignLastValue"}, "transformation_params": {"0": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false, "threshold": 1, "threshold_method": "mean", "mean_type": "arithmetic"}}}
7 - LastValueNaive with avg smape 124.13 in 0.01s:
Model Number: 8 of 15 with model LastValueNaive for Validation 1 with params {} and transformations {"fillna": "ffill", "transformations": {"0": "AlignLastValue", "1": "QuantileTransformer"}, "transformation_params": {"0": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false, "threshold": 1, "threshold_method": "max", "mean_type": "arithmetic"}, "1": {"output_distribution": "uniform", "n_quantiles": 1000}}}
8 - LastValueNaive with avg smape 124.13 in 0.03s:
Model Number: 9 of 15 with model ConstantNaive for Validation 1 with params {"constant": 0} and transformations {"fillna": "ffill", "transformations": {"0": "AlignLastValue"}, "transformation_params": {"0": {"rows": 4, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false, "threshold": null, "threshold_method": "max", "mean_type": "arithmetic"}}}
9 - ConstantNaive with avg smape 124.49 in 0.01s:
Model Number: 10 of 15 with model ConstantNaive for Validation 1 with params {"constant": 0} and transformations {"fillna": "ffill_mean_biased", "transformations": {"0": "AlignLastValue", "1": "MeanPercentSplitter", "2": "AlignLastValue"}, "transformation_params": {"0": {"rows": 4, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false, "threshold": null, "threshold_method": "max", "mean_type": "arithmetic"}, "1": {"window": 3, "min_mean_threshold": 0.1}, "2": {"rows": 7, "lag": 168, "method": "additive", "strength": 1.0, "first_value_only": false, "threshold": null, "threshold_method": "max", "mean_type": "arithmetic"}}}
10 - ConstantNaive with avg smape 124.49 in 0.01s:
Model Number: 11 of 15 with model SeasonalityMotif for Validation 1 with params {"window": 5, "point_method": "trimmed_mean_40", "distance_metric": "mae", "k": 5, "datepart_method": ["simple_binarized_poly"], "independent": true} and transformations {"fillna": "rolling_mean", "transformations": {"0": "Constraint", "1": "QuantileTransformer", "2": "AlignLastValue"}, "transformation_params": {"0": {"constraint_method": "historic_diff", "constraint_direction": "lower", "constraint_regularization": 1.0, "constraint_value": 0.2, "bounds_only": false, "fillna": null}, "1": {"output_distribution": "uniform", "n_quantiles": 43}, "2": {"rows": 1, "lag": 1, "method": "additive", "strength": 0.2, "first_value_only": false, "threshold": null, "threshold_method": "max"}}}
11 - SeasonalityMotif with avg smape 124.57 in 0.06s:
Model Number: 12 of 15 with model ConstantNaive for Validation 1 with params {"constant": 0} and transformations {"fillna": "ffill", "transformations": {"0": "PowerTransformer", "1": "QuantileTransformer", "2": "SeasonalDifference"}, "transformation_params": {"0": {}, "1": {"output_distribution": "uniform", "n_quantiles": 1000}, "2": {"lag_1": 7, "method": "LastValue"}}}
12 - ConstantNaive with avg smape 124.88 in 0.04s:
Model Number: 13 of 15 with model SeasonalNaive for Validation 1 with params {"method": "lastvalue", "lag_1": 96, "lag_2": 1} and transformations {"fillna": "rolling_mean_24", "transformations": {"0": "ClipOutliers", "1": "QuantileTransformer", "2": "HistoricValues", "3": "AlignLastValue"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 5, "fillna": null}, "1": {"output_distribution": "uniform", "n_quantiles": 1000}, "2": {"window": 364}, "3": {"rows": 1, "lag": 7, "method": "additive", "strength": 1.0, "first_value_only": false, "threshold": 1, "threshold_method": "max", "mean_type": "arithmetic"}}}
13 - SeasonalNaive with avg smape 130.83 in 0.06s:
Model Number: 14 of 15 with model AverageValueNaive for Validation 1 with params {"method": "Mean", "window": null} and transformations {"fillna": "mean", "transformations": {"0": "ClipOutliers", "1": "ClipOutliers", "2": "DifferencedTransformer", "3": "ReplaceConstant"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 3, "fillna": null}, "1": {"method": "clip", "std_threshold": 3.5, "fillna": null}, "2": {}, "3": {"constant": 0, "reintroduction_model": {"model": "SGD", "model_params": {"learning_rate": 0.15, "max_iter": 6, "l2": 0.0001, "column_batch_size": 256, "probability_threshold": 0.5}, "vectorized": true, "datepart_method": "simple_poly"}, "fillna": "pchip"}}}
14 - AverageValueNaive with avg smape 124.59 in 0.02s:
Model Number: 15 of 15 with model AverageValueNaive for Validation 1 with params {"method": "Mean", "window": null} and transformations {"fillna": "mean", "transformations": {"0": "ClipOutliers", "1": "QuantileTransformer", "2": "DifferencedTransformer"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 3, "fillna": null}, "1": {"output_distribution": "uniform", "n_quantiles": 1000}, "2": {}}}
15 - AverageValueNaive with avg smape 134.34 in 0.02s:
Validation Round: 2
Validation train index is DatetimeIndex(['2020-02-05', '2020-02-06', '2020-02-07', '2020-02-08',
               '2020-02-09', '2020-02-10', '2020-02-11', '2020-02-12',
               '2020-02-13', '2020-02-14',
               ...
               '2023-01-29', '2023-01-30', '2023-01-31', '2023-02-01',
               '2023-02-02', '2023-02-03', '2023-02-04', '2023-02-05',
               '2023-02-06', '2023-02-07'],
              dtype='datetime64[ns]', length=1099, freq=None)
Model Number: 1 of 15 with model SectionalMotif for Validation 2 with params {"window": 5, "point_method": "midhinge", "distance_metric": "mahalanobis", "include_differenced": true, "k": 5, "stride_size": 1, "regression_type": null, "comparison_transformation": null, "combination_transformation": null} and transformations {"fillna": "quadratic", "transformations": {"0": "ClipOutliers", "1": "AlignLastValue"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 3.5, "fillna": null}, "1": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false, "threshold": 1, "threshold_method": "max", "mean_type": "arithmetic"}}}
📈 1 - SectionalMotif with avg smape 120.3 in 0.02s:
Model Number: 2 of 15 with model SectionalMotif for Validation 2 with params {"window": 7, "point_method": "midhinge", "distance_metric": "russellrao", "include_differenced": true, "k": 5, "stride_size": 1, "regression_type": null, "comparison_transformation": null, "combination_transformation": null} and transformations {"fillna": "ffill", "transformations": {"0": "BKBandpassFilter", "1": "AlignLastValue", "2": "HistoricValues"}, "transformation_params": {"0": {"low": 4, "high": 32, "K": 6, "lanczos_factor": false, "return_diff": true, "on_transform": true, "on_inverse": false}, "1": {"rows": 1, "lag": 2, "method": "additive", "strength": 1.0, "first_value_only": false}, "2": {"window": null}}}
2 - SectionalMotif with avg smape 120.54 in 0.01s:
Model Number: 3 of 15 with model SeasonalityMotif for Validation 2 with params {"window": 10, "point_method": "closest", "distance_metric": "mae", "k": 1, "datepart_method": ["weekdaymonthofyear", "quarter", "dayofweek"], "independent": false} and transformations {"fillna": "ffill", "transformations": {"0": "ClipOutliers", "1": "Detrend", "2": "UpscaleDownscaleTransformer", "3": "AlignLastValue"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 2, "fillna": null}, "1": {"model": "Linear", "phi": 1, "window": 90, "transform_dict": null}, "2": {"mode": "upscale", "factor": 1, "down_method": "mean", "fill_method": "ffill"}, "3": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false, "threshold": 1, "threshold_method": "max", "mean_type": "arithmetic"}}}
3 - SeasonalityMotif with avg smape 129.22 in 0.07s:
Model Number: 4 of 15 with model SectionalMotif for Validation 2 with params {"window": 5, "point_method": "midhinge", "distance_metric": "canberra", "include_differenced": true, "k": 10, "stride_size": 2, "regression_type": null, "comparison_transformation": null, "combination_transformation": null} and transformations {"fillna": "ffill", "transformations": {"0": "RollingMeanTransformer", "1": "Detrend", "2": "UpscaleDownscaleTransformer", "3": "AlignLastValue"}, "transformation_params": {"0": {"fixed": true, "window": 12, "macro_micro": false, "center": false, "mean_type": "arithmetic"}, "1": {"model": "Linear", "phi": 1, "window": 90, "transform_dict": null}, "2": {"mode": "upscale", "factor": 1, "down_method": "mean", "fill_method": "ffill"}, "3": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false, "threshold": 1, "threshold_method": "max", "mean_type": "arithmetic"}}}
4 - SectionalMotif with avg smape 129.51 in 0.02s:
Model Number: 5 of 15 with model BasicLinearModel for Validation 2 with params {"datepart_method": "recurring", "changepoint_spacing": 28, "changepoint_distance_end": 90, "regression_type": null, "lambda_": 0.01, "trend_phi": 0.98, "holiday_countries_used": true} and transformations {"fillna": "ffill", "transformations": {"0": "Slice", "1": "ClipOutliers", "2": "SeasonalDifference", "3": "LevelShiftTransformer", "4": "LevelShiftTransformer", "5": "PositiveShift"}, "transformation_params": {"0": {"method": 0.2}, "1": {"method": "clip", "std_threshold": 3, "fillna": null}, "2": {"lag_1": 12, "method": 5}, "3": {"window_size": 7, "alpha": 2.0, "grouping_forward_limit": 5, "max_level_shifts": 30, "alignment": "rolling_diff_3nn"}, "4": {"window_size": 7, "alpha": 2.0, "grouping_forward_limit": 5, "max_level_shifts": 30, "alignment": "rolling_diff_3nn"}, "5": {}}}
5 - BasicLinearModel with avg smape 123.39 in 0.16s:
Model Number: 6 of 15 with model LastValueNaive for Validation 2 with params {} and transformations {"fillna": "ffill", "transformations": {"0": "AlignLastValue", "1": "Detrend", "2": "LevelShiftTransformer", "3": "AlignLastValue"}, "transformation_params": {"0": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false, "threshold": 1, "threshold_method": "max", "mean_type": "arithmetic"}, "1": {"model": "GLS", "phi": 0.999, "window": null, "transform_dict": null}, "2": {"window_size": 90, "alpha": 2.5, "grouping_forward_limit": 5, "max_level_shifts": 5, "alignment": "last_value", "output": "univariate", "remove_at_shift": false, "shift_remove_window": 1, "shift_fillna": "linear", "window_method": "diff_overlap"}, "3": {"rows": 1, "lag": 7, "method": "additive", "strength": 1.0, "first_value_only": false, "threshold": 1, "threshold_method": "max", "mean_type": "arithmetic"}}}
6 - LastValueNaive with avg smape 123.34 in 0.04s:
Model Number: 7 of 15 with model LastValueNaive for Validation 2 with params {} and transformations {"fillna": "ffill", "transformations": {"0": "AlignLastValue"}, "transformation_params": {"0": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false, "threshold": 1, "threshold_method": "mean", "mean_type": "arithmetic"}}}
7 - LastValueNaive with avg smape 123.37 in 0.01s:
Model Number: 8 of 15 with model LastValueNaive for Validation 2 with params {} and transformations {"fillna": "ffill", "transformations": {"0": "AlignLastValue", "1": "QuantileTransformer"}, "transformation_params": {"0": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false, "threshold": 1, "threshold_method": "max", "mean_type": "arithmetic"}, "1": {"output_distribution": "uniform", "n_quantiles": 1000}}}
8 - LastValueNaive with avg smape 123.37 in 0.03s:
Model Number: 9 of 15 with model ConstantNaive for Validation 2 with params {"constant": 0} and transformations {"fillna": "ffill", "transformations": {"0": "AlignLastValue"}, "transformation_params": {"0": {"rows": 4, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false, "threshold": null, "threshold_method": "max", "mean_type": "arithmetic"}}}
9 - ConstantNaive with avg smape 123.68 in 0.01s:
Model Number: 10 of 15 with model ConstantNaive for Validation 2 with params {"constant": 0} and transformations {"fillna": "ffill_mean_biased", "transformations": {"0": "AlignLastValue", "1": "MeanPercentSplitter", "2": "AlignLastValue"}, "transformation_params": {"0": {"rows": 4, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false, "threshold": null, "threshold_method": "max", "mean_type": "arithmetic"}, "1": {"window": 3, "min_mean_threshold": 0.1}, "2": {"rows": 7, "lag": 168, "method": "additive", "strength": 1.0, "first_value_only": false, "threshold": null, "threshold_method": "max", "mean_type": "arithmetic"}}}
10 - ConstantNaive with avg smape 123.68 in 0.01s:
Model Number: 11 of 15 with model SeasonalityMotif for Validation 2 with params {"window": 5, "point_method": "trimmed_mean_40", "distance_metric": "mae", "k": 5, "datepart_method": ["simple_binarized_poly"], "independent": true} and transformations {"fillna": "rolling_mean", "transformations": {"0": "Constraint", "1": "QuantileTransformer", "2": "AlignLastValue"}, "transformation_params": {"0": {"constraint_method": "historic_diff", "constraint_direction": "lower", "constraint_regularization": 1.0, "constraint_value": 0.2, "bounds_only": false, "fillna": null}, "1": {"output_distribution": "uniform", "n_quantiles": 43}, "2": {"rows": 1, "lag": 1, "method": "additive", "strength": 0.2, "first_value_only": false, "threshold": null, "threshold_method": "max"}}}
11 - SeasonalityMotif with avg smape 123.84 in 0.06s:
Model Number: 12 of 15 with model ConstantNaive for Validation 2 with params {"constant": 0} and transformations {"fillna": "ffill", "transformations": {"0": "PowerTransformer", "1": "QuantileTransformer", "2": "SeasonalDifference"}, "transformation_params": {"0": {}, "1": {"output_distribution": "uniform", "n_quantiles": 1000}, "2": {"lag_1": 7, "method": "LastValue"}}}
12 - ConstantNaive with avg smape 124.02 in 0.04s:
Model Number: 13 of 15 with model SeasonalNaive for Validation 2 with params {"method": "lastvalue", "lag_1": 96, "lag_2": 1} and transformations {"fillna": "rolling_mean_24", "transformations": {"0": "ClipOutliers", "1": "QuantileTransformer", "2": "HistoricValues", "3": "AlignLastValue"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 5, "fillna": null}, "1": {"output_distribution": "uniform", "n_quantiles": 1000}, "2": {"window": 364}, "3": {"rows": 1, "lag": 7, "method": "additive", "strength": 1.0, "first_value_only": false, "threshold": 1, "threshold_method": "max", "mean_type": "arithmetic"}}}
13 - SeasonalNaive with avg smape 122.99 in 0.06s:
Model Number: 14 of 15 with model AverageValueNaive for Validation 2 with params {"method": "Mean", "window": null} and transformations {"fillna": "mean", "transformations": {"0": "ClipOutliers", "1": "ClipOutliers", "2": "DifferencedTransformer", "3": "ReplaceConstant"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 3, "fillna": null}, "1": {"method": "clip", "std_threshold": 3.5, "fillna": null}, "2": {}, "3": {"constant": 0, "reintroduction_model": {"model": "SGD", "model_params": {"learning_rate": 0.15, "max_iter": 6, "l2": 0.0001, "column_batch_size": 256, "probability_threshold": 0.5}, "vectorized": true, "datepart_method": "simple_poly"}, "fillna": "pchip"}}}
14 - AverageValueNaive with avg smape 123.53 in 0.02s:
Model Number: 15 of 15 with model AverageValueNaive for Validation 2 with params {"method": "Mean", "window": null} and transformations {"fillna": "mean", "transformations": {"0": "ClipOutliers", "1": "QuantileTransformer", "2": "DifferencedTransformer"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 3, "fillna": null}, "1": {"output_distribution": "uniform", "n_quantiles": 1000}, "2": {}}}
15 - AverageValueNaive with avg smape 122.24 in 0.02s:
Validation Round: 0
Validation train index is DatetimeIndex(['2020-02-05', '2020-02-06', '2020-02-07', '2020-02-08',
               '2020-02-09', '2020-02-10', '2020-02-11', '2020-02-12',
               '2020-02-13', '2020-02-14',
               ...
               '2023-03-27', '2023-03-28', '2023-03-29', '2023-03-30',
               '2023-03-31', '2023-04-01', '2023-04-02', '2023-04-03',
               '2023-04-04', '2023-04-05'],
              dtype='datetime64[ns]', length=1156, freq=None)
Model Number: 1 of 1 with model Ensemble for Validation 1 horizontal ensemble validations with params {"model_name": "Horizontal", "model_count": 3, "model_metric": "Score", "models": {"1cd85fce3f053dc4bcd2ca7316fefe2e": {"Model": "ConstantNaive", "ModelParameters": "{\"constant\": 0}", "TransformationParameters": "{\"fillna\": \"ffill\", \"transformations\": {\"0\": \"AlignLastValue\"}, \"transformation_params\": {\"0\": {\"rows\": 4, \"lag\": 1, \"method\": \"additive\", \"strength\": 1.0, \"first_value_only\": false, \"threshold\": null, \"threshold_method\": \"max\", \"mean_type\": \"arithmetic\"}}}"}, "0e256ae8f87bbbce9a99e0321cacc456": {"Model": "SeasonalityMotif", "ModelParameters": "{\"window\": 10, \"point_method\": \"closest\", \"distance_metric\": \"mae\", \"k\": 1, \"datepart_method\": [\"weekdaymonthofyear\", \"quarter\", \"dayofweek\"], \"independent\": false}", "TransformationParameters": "{\"fillna\": \"ffill\", \"transformations\": {\"0\": \"ClipOutliers\", \"1\": \"Detrend\", \"2\": \"UpscaleDownscaleTransformer\", \"3\": \"AlignLastValue\"}, \"transformation_params\": {\"0\": {\"method\": \"clip\", \"std_threshold\": 2, \"fillna\": null}, \"1\": {\"model\": \"Linear\", \"phi\": 1, \"window\": 90, \"transform_dict\": null}, \"2\": {\"mode\": \"upscale\", \"factor\": 1, \"down_method\": \"mean\", \"fill_method\": \"ffill\"}, \"3\": {\"rows\": 1, \"lag\": 1, \"method\": \"additive\", \"strength\": 1.0, \"first_value_only\": false, \"threshold\": 1, \"threshold_method\": \"max\", \"mean_type\": \"arithmetic\"}}}"}, "bd4a4d897ff73fd8876795918e01deec": {"Model": "SectionalMotif", "ModelParameters": "{\"window\": 5, \"point_method\": \"midhinge\", \"distance_metric\": \"mahalanobis\", \"include_differenced\": true, \"k\": 5, \"stride_size\": 1, \"regression_type\": null, \"comparison_transformation\": null, \"combination_transformation\": null}", "TransformationParameters": "{\"fillna\": \"quadratic\", \"transformations\": {\"0\": \"ClipOutliers\", \"1\": \"AlignLastValue\"}, \"transformation_params\": {\"0\": {\"method\": \"clip\", \"std_threshold\": 3.5, \"fillna\": null}, \"1\": {\"rows\": 1, \"lag\": 1, \"method\": \"additive\", \"strength\": 1.0, \"first_value_only\": false, \"threshold\": 1, \"threshold_method\": \"max\", \"mean_type\": \"arithmetic\"}}}"}}, "series": {"PC1": "bd4a4d897ff73fd8876795918e01deec", "PC2": "0e256ae8f87bbbce9a99e0321cacc456", "PC3": "bd4a4d897ff73fd8876795918e01deec", "PC4": "0e256ae8f87bbbce9a99e0321cacc456", "PC5": "bd4a4d897ff73fd8876795918e01deec", "PC6": "bd4a4d897ff73fd8876795918e01deec", "PC7": "0e256ae8f87bbbce9a99e0321cacc456", "PC8": "1cd85fce3f053dc4bcd2ca7316fefe2e", "PC9": "bd4a4d897ff73fd8876795918e01deec", "PC10": "1cd85fce3f053dc4bcd2ca7316fefe2e"}} and transformations {} horizontal ensemble validations
Ensemble Horizontal component 1 of 3 ConstantNaive started
Ensemble Horizontal component 2 of 3 SeasonalityMotif started
Ensemble Horizontal component 3 of 3 SectionalMotif started
📈 1 - Ensemble with avg smape 106.29 in 0.10s:
2 - Ensemble with avg smape 120.27 in 0.11s:
📈 3 - Ensemble with avg smape 21.02 in 0.13s:
4 - Ensemble with avg smape 25.34 in 0.14s:
5 - Ensemble with avg smape 35.95 in 0.16s:
6 - Ensemble with avg smape 26.56 in 0.17s:
Template Eval Error: Traceback (most recent call last):
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 8295, in _fit
    df = self._fit_one(df, i)
         ^^^^^^^^^^^^^^^^^^^^
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 8273, in _fit_one
    param=self.transformation_params[i],
          ~~~~~~~~~~~~~~~~~~~~~~~~~~^^^
KeyError: '2'

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 2296, in TemplateWizard
    transformer_object.fit(df_train)
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 8310, in fit
    self._fit(df)
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 8300, in _fit
    raise Exception(err_str) from e
Exception: Transformer HistoricValues failed on fit from params fake_date {'0': {'rows': 1, 'lag': 1, 'method': 'additive', 'strength': 1.0, 'first_value_only': False, 'threshold': 10, 'threshold_method': 'mean'}, '1': {'window': 28}} with error KeyError('2')
 in model 6 in generation 0: Ensemble
Validation Round: 1
Validation train index is DatetimeIndex(['2020-02-05', '2020-02-06', '2020-02-07', '2020-02-08',
               '2020-02-09', '2020-02-10', '2020-02-11', '2020-02-12',
               '2020-02-13', '2020-02-14',
               ...
               '2023-02-26', '2023-02-27', '2023-02-28', '2023-03-01',
               '2023-03-02', '2023-03-03', '2023-03-04', '2023-03-05',
               '2023-03-06', '2023-03-07'],
              dtype='datetime64[ns]', length=1127, freq=None)
Model Number: 1 of 1 with model Ensemble for Validation 2 horizontal ensemble validations with params {"model_name": "Horizontal", "model_count": 3, "model_metric": "Score", "models": {"1cd85fce3f053dc4bcd2ca7316fefe2e": {"Model": "ConstantNaive", "ModelParameters": "{\"constant\": 0}", "TransformationParameters": "{\"fillna\": \"ffill\", \"transformations\": {\"0\": \"AlignLastValue\"}, \"transformation_params\": {\"0\": {\"rows\": 4, \"lag\": 1, \"method\": \"additive\", \"strength\": 1.0, \"first_value_only\": false, \"threshold\": null, \"threshold_method\": \"max\", \"mean_type\": \"arithmetic\"}}}"}, "0e256ae8f87bbbce9a99e0321cacc456": {"Model": "SeasonalityMotif", "ModelParameters": "{\"window\": 10, \"point_method\": \"closest\", \"distance_metric\": \"mae\", \"k\": 1, \"datepart_method\": [\"weekdaymonthofyear\", \"quarter\", \"dayofweek\"], \"independent\": false}", "TransformationParameters": "{\"fillna\": \"ffill\", \"transformations\": {\"0\": \"ClipOutliers\", \"1\": \"Detrend\", \"2\": \"UpscaleDownscaleTransformer\", \"3\": \"AlignLastValue\"}, \"transformation_params\": {\"0\": {\"method\": \"clip\", \"std_threshold\": 2, \"fillna\": null}, \"1\": {\"model\": \"Linear\", \"phi\": 1, \"window\": 90, \"transform_dict\": null}, \"2\": {\"mode\": \"upscale\", \"factor\": 1, \"down_method\": \"mean\", \"fill_method\": \"ffill\"}, \"3\": {\"rows\": 1, \"lag\": 1, \"method\": \"additive\", \"strength\": 1.0, \"first_value_only\": false, \"threshold\": 1, \"threshold_method\": \"max\", \"mean_type\": \"arithmetic\"}}}"}, "bd4a4d897ff73fd8876795918e01deec": {"Model": "SectionalMotif", "ModelParameters": "{\"window\": 5, \"point_method\": \"midhinge\", \"distance_metric\": \"mahalanobis\", \"include_differenced\": true, \"k\": 5, \"stride_size\": 1, \"regression_type\": null, \"comparison_transformation\": null, \"combination_transformation\": null}", "TransformationParameters": "{\"fillna\": \"quadratic\", \"transformations\": {\"0\": \"ClipOutliers\", \"1\": \"AlignLastValue\"}, \"transformation_params\": {\"0\": {\"method\": \"clip\", \"std_threshold\": 3.5, \"fillna\": null}, \"1\": {\"rows\": 1, \"lag\": 1, \"method\": \"additive\", \"strength\": 1.0, \"first_value_only\": false, \"threshold\": 1, \"threshold_method\": \"max\", \"mean_type\": \"arithmetic\"}}}"}}, "series": {"PC1": "bd4a4d897ff73fd8876795918e01deec", "PC2": "0e256ae8f87bbbce9a99e0321cacc456", "PC3": "bd4a4d897ff73fd8876795918e01deec", "PC4": "0e256ae8f87bbbce9a99e0321cacc456", "PC5": "bd4a4d897ff73fd8876795918e01deec", "PC6": "bd4a4d897ff73fd8876795918e01deec", "PC7": "0e256ae8f87bbbce9a99e0321cacc456", "PC8": "1cd85fce3f053dc4bcd2ca7316fefe2e", "PC9": "bd4a4d897ff73fd8876795918e01deec", "PC10": "1cd85fce3f053dc4bcd2ca7316fefe2e"}} and transformations {} horizontal ensemble validations
Ensemble Horizontal component 1 of 3 ConstantNaive started
Ensemble Horizontal component 2 of 3 SeasonalityMotif started
Ensemble Horizontal component 3 of 3 SectionalMotif started
📈 1 - Ensemble with avg smape 120.31 in 0.10s:
2 - Ensemble with avg smape 120.31 in 0.11s:
📈 3 - Ensemble with avg smape 12.31 in 0.13s:
4 - Ensemble with avg smape 78.14 in 0.14s:
5 - Ensemble with avg smape 21.02 in 0.16s:
6 - Ensemble with avg smape 13.49 in 0.17s:
Template Eval Error: Traceback (most recent call last):
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 8295, in _fit
    df = self._fit_one(df, i)
         ^^^^^^^^^^^^^^^^^^^^
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 8273, in _fit_one
    param=self.transformation_params[i],
          ~~~~~~~~~~~~~~~~~~~~~~~~~~^^^
KeyError: '2'

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 2296, in TemplateWizard
    transformer_object.fit(df_train)
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 8310, in fit
    self._fit(df)
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 8300, in _fit
    raise Exception(err_str) from e
Exception: Transformer HistoricValues failed on fit from params fake_date {'0': {'rows': 1, 'lag': 1, 'method': 'additive', 'strength': 1.0, 'first_value_only': False, 'threshold': 10, 'threshold_method': 'mean'}, '1': {'window': 28}} with error KeyError('2')
 in model 6 in generation 0: Ensemble
Validation Round: 2
Validation train index is DatetimeIndex(['2020-02-05', '2020-02-06', '2020-02-07', '2020-02-08',
               '2020-02-09', '2020-02-10', '2020-02-11', '2020-02-12',
               '2020-02-13', '2020-02-14',
               ...
               '2023-01-28', '2023-01-29', '2023-01-30', '2023-01-31',
               '2023-02-01', '2023-02-02', '2023-02-03', '2023-02-04',
               '2023-02-05', '2023-02-06'],
              dtype='datetime64[ns]', length=1098, freq=None)
Model Number: 1 of 1 with model Ensemble for Validation 3 horizontal ensemble validations with params {"model_name": "Horizontal", "model_count": 3, "model_metric": "Score", "models": {"1cd85fce3f053dc4bcd2ca7316fefe2e": {"Model": "ConstantNaive", "ModelParameters": "{\"constant\": 0}", "TransformationParameters": "{\"fillna\": \"ffill\", \"transformations\": {\"0\": \"AlignLastValue\"}, \"transformation_params\": {\"0\": {\"rows\": 4, \"lag\": 1, \"method\": \"additive\", \"strength\": 1.0, \"first_value_only\": false, \"threshold\": null, \"threshold_method\": \"max\", \"mean_type\": \"arithmetic\"}}}"}, "0e256ae8f87bbbce9a99e0321cacc456": {"Model": "SeasonalityMotif", "ModelParameters": "{\"window\": 10, \"point_method\": \"closest\", \"distance_metric\": \"mae\", \"k\": 1, \"datepart_method\": [\"weekdaymonthofyear\", \"quarter\", \"dayofweek\"], \"independent\": false}", "TransformationParameters": "{\"fillna\": \"ffill\", \"transformations\": {\"0\": \"ClipOutliers\", \"1\": \"Detrend\", \"2\": \"UpscaleDownscaleTransformer\", \"3\": \"AlignLastValue\"}, \"transformation_params\": {\"0\": {\"method\": \"clip\", \"std_threshold\": 2, \"fillna\": null}, \"1\": {\"model\": \"Linear\", \"phi\": 1, \"window\": 90, \"transform_dict\": null}, \"2\": {\"mode\": \"upscale\", \"factor\": 1, \"down_method\": \"mean\", \"fill_method\": \"ffill\"}, \"3\": {\"rows\": 1, \"lag\": 1, \"method\": \"additive\", \"strength\": 1.0, \"first_value_only\": false, \"threshold\": 1, \"threshold_method\": \"max\", \"mean_type\": \"arithmetic\"}}}"}, "bd4a4d897ff73fd8876795918e01deec": {"Model": "SectionalMotif", "ModelParameters": "{\"window\": 5, \"point_method\": \"midhinge\", \"distance_metric\": \"mahalanobis\", \"include_differenced\": true, \"k\": 5, \"stride_size\": 1, \"regression_type\": null, \"comparison_transformation\": null, \"combination_transformation\": null}", "TransformationParameters": "{\"fillna\": \"quadratic\", \"transformations\": {\"0\": \"ClipOutliers\", \"1\": \"AlignLastValue\"}, \"transformation_params\": {\"0\": {\"method\": \"clip\", \"std_threshold\": 3.5, \"fillna\": null}, \"1\": {\"rows\": 1, \"lag\": 1, \"method\": \"additive\", \"strength\": 1.0, \"first_value_only\": false, \"threshold\": 1, \"threshold_method\": \"max\", \"mean_type\": \"arithmetic\"}}}"}}, "series": {"PC1": "bd4a4d897ff73fd8876795918e01deec", "PC2": "0e256ae8f87bbbce9a99e0321cacc456", "PC3": "bd4a4d897ff73fd8876795918e01deec", "PC4": "0e256ae8f87bbbce9a99e0321cacc456", "PC5": "bd4a4d897ff73fd8876795918e01deec", "PC6": "bd4a4d897ff73fd8876795918e01deec", "PC7": "0e256ae8f87bbbce9a99e0321cacc456", "PC8": "1cd85fce3f053dc4bcd2ca7316fefe2e", "PC9": "bd4a4d897ff73fd8876795918e01deec", "PC10": "1cd85fce3f053dc4bcd2ca7316fefe2e"}} and transformations {} horizontal ensemble validations
Ensemble Horizontal component 1 of 3 ConstantNaive started
Ensemble Horizontal component 2 of 3 SeasonalityMotif started
Ensemble Horizontal component 3 of 3 SectionalMotif started
📈 1 - Ensemble with avg smape 120.3 in 0.10s:
2 - Ensemble with avg smape 120.3 in 0.11s:
📈 3 - Ensemble with avg smape 8.25 in 0.13s:
4 - Ensemble with avg smape 75.01 in 0.14s:
5 - Ensemble with avg smape 19.41 in 0.16s:
6 - Ensemble with avg smape 8.92 in 0.17s:
Template Eval Error: Traceback (most recent call last):
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 8295, in _fit
    df = self._fit_one(df, i)
         ^^^^^^^^^^^^^^^^^^^^
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 8273, in _fit_one
    param=self.transformation_params[i],
          ~~~~~~~~~~~~~~~~~~~~~~~~~~^^^
KeyError: '2'

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 2296, in TemplateWizard
    transformer_object.fit(df_train)
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 8310, in fit
    self._fit(df)
  File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 8300, in _fit
    raise Exception(err_str) from e
Exception: Transformer HistoricValues failed on fit from params fake_date {'0': {'rows': 1, 'lag': 1, 'method': 'additive', 'strength': 1.0, 'first_value_only': False, 'threshold': 10, 'threshold_method': 'mean'}, '1': {'window': 28}} with error KeyError('2')
 in model 6 in generation 0: Ensemble
Ensemble Horizontal component 1 of 3 ConstantNaive started
Ensemble Horizontal component 2 of 3 SeasonalityMotif started
Ensemble Horizontal component 3 of 3 SectionalMotif started

[12]:
PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 PC9 PC10
Date
2023-06-03 5.901136 -2.237191 0.598464 -0.134111 0.953501 0.009707 -0.367141 -0.62687 0.080097 -0.32227
2023-06-04 5.910911 -2.237191 0.600318 -0.134111 0.956277 0.009707 -0.367141 -0.62687 0.081829 -0.32227
2023-06-05 5.920685 -2.237191 0.602172 -0.134111 0.959052 0.009707 -0.367141 -0.62687 0.083560 -0.32227
2023-06-06 5.930460 -2.237191 0.604026 -0.134111 0.961827 0.009707 -0.367141 -0.62687 0.085292 -0.32227
2023-06-07 5.940234 -2.237191 0.605880 -0.134111 0.964603 0.009707 -0.367141 -0.62687 0.087023 -0.32227

2-3. Prediction of ODE parameter values

Now we have Y (estimated ODE parameter values) and X (estimated/forecasted indicator values without multicollinearity), we can predict ODE parameter values of future phases using ODEScenario().predict(days=<int>, name=<str>, seed=0, X=<pandas.DataFrame>). The new scenario is named “Predicted_with_X” here.

[13]:

snr.build_with_template(name="Predicted_with_X", template="Baseline")
snr.predict(days=30, name="Predicted_with_X", seed=0, X=future_df);

3. Compare scenarios

As explained with Tutorial: Scenario analysis, we can compare scenarios.

[14]:

# Adjust the last date, appending a phase
snr.append();

[15]:

# Compare reproduction number
ymin = snr.compare_param("Rt", date_range=(future_start_date, None), display=False).min().min()
snr.compare_param("Rt", date_range=(future_start_date, None), ylim=(ymin, None));
_images/06_prediction_31_0.png

Note that minimum value of y in the figure was changed to focus on the differences of the scenarios.

[16]:

ymin_value = snr.compare_cases("Confirmed", date_range=(future_start_date, None), display=False).Predicted.min()
snr.compare_cases("Confirmed", date_range=(future_start_date, None), ylim=(ymin_value, None));
_images/06_prediction_33_0.png 
[17]:

# Show representative values
snr.describe()

[17]:
max(Infected) argmax(Infected) Confirmed on 07Jul2023 Infected on 07Jul2023 Fatal on 07Jul2023
Baseline 3724702.0 2022-08-15 34734646.0 281837.0 76573.0
Predicted 3724702.0 2022-08-15 34380272.0 98047.0 76663.0
Predicted_with_X 3724702.0 2022-08-15 34734646.0 281837.0 76573.0

Thank you!