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0.6.15 #247

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Jul 22, 2024
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0.6.15 #247

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42f6d69
0.6.15 a1
winedarksea May 18, 2024
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0.6.15 a5
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0.6.15 a6
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0.6.15 a7
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0.6.15 a8
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0.6.15 a12
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0.6.15 a12
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@winedarksea
winedarksea committed Jun 13, 2024
commit 30407fbfe20493710c1633285bb0e3e143d2d92c
16 changes: 12 additions & 4 deletions autots/templates/general.py
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Original file line number Diff line number Diff line change
Expand Up @@ -449,10 +449,10 @@
'TransformationParameters': '{"fillna": "pad", "transformations": {"0": "EWMAFilter", "1": "LevelShiftTransformer", "2": "StandardScaler", "3": "DatepartRegression"}, "transformation_params": {"0": {"span": 7}, "1": {"window_size": 7, "alpha": 2.0, "grouping_forward_limit": 2, "max_level_shifts": 5, "alignment": "average"}, "2": {}, "3": {"regression_model": {"model": "ElasticNet", "model_params": {}}, "datepart_method": "recurring", "polynomial_degree": null, "transform_dict": {"fillna": null, "transformations": {"0": "ScipyFilter"}, "transformation_params": {"0": {"method": "savgol_filter", "method_args": {"window_length": 31, "polyorder": 3, "deriv": 0, "mode": "interp"}}}}, "holiday_countries_used": false}}}',
'Ensemble': 0,
},
"72": {
"72": { # optimized on M5, 58.5 SMAPE
'Model': 'NeuralForecast',
'ModelParameters': '''{"model": "MLP", "scaler_type": "minmax", "loss": "MQLoss", "learning_rate": 0.001, "max_steps": 100, "input_size": 28, "model_args": {"num_layers": 1, "hidden_size": 2560}, "regression_type": null}''',
'TransformationParameters': '''{"fillna": "SeasonalityMotifImputerLinMix", "transformations": {"0": "ClipOutliers", "1": "QuantileTransformer", "2": "SeasonalDifference", "3": "RobustScaler", "4": "ClipOutliers", "5": "MaxAbsScaler"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 3.5, "fillna": null}, "1": {"output_distribution": "normal", "n_quantiles": 100}, "2": {"lag_1": 7, "method": "Mean"}, "3": {}, "4": {"method": "clip", "std_threshold": 4, "fillna": null}, "5": {}}}''',
'TransformationParameters': '''{"fillna": "ffill", "transformations": {"0": "ClipOutliers", "1": "QuantileTransformer", "2": "SeasonalDifference", "3": "RobustScaler", "4": "ClipOutliers", "5": "MaxAbsScaler"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 3.5, "fillna": null}, "1": {"output_distribution": "normal", "n_quantiles": 100}, "2": {"lag_1": 7, "method": "Mean"}, "3": {}, "4": {"method": "clip", "std_threshold": 4, "fillna": null}, "5": {}}}''',
'Ensemble': 0,
},
"73": { # from production_example, mosaic most common, 2024-02-21
Expand All @@ -476,8 +476,16 @@
"76": {
'Model': 'NVAR',
'ModelParameters': '{"k": 2, "ridge_param": 2e-06, "warmup_pts": 1, "seed_pts": 1, "seed_weighted": null, "batch_size": 5, "batch_method": "std_sorted"}',
'TransformationParameters': '{"fillna": "ffill", "transformations": {"0": "HolidayTransformer", "1": "PositiveShift"}, "transformation_params": {"0": {"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, "anomaly_detector_params": {"method": "rolling_zscore", "method_params": {"distribution": "uniform", "alpha": 0.03, "rolling_periods": 300, "center": true}, "fillna": "ffill", "transform_dict": {"fillna": "nearest", "transformations": {"0": null}, "transformation_params": {"0": {}}}, "isolated_only": false}, "remove_excess_anomalies": true, "impact": "datepart_regression", "regression_params": {"regression_model": {"model": "ElasticNet", "model_params": {"l1_ratio": 0.1, "fit_intercept": true, "selection": "cyclic"}}, "datepart_method": "simple", "polynomial_degree": null, "transform_dict": null, "holiday_countries_used": false}}, "1": {}}}'
}
'TransformationParameters': '{"fillna": "ffill", "transformations": {"0": "HolidayTransformer", "1": "PositiveShift"}, "transformation_params": {"0": {"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, "anomaly_detector_params": {"method": "rolling_zscore", "method_params": {"distribution": "uniform", "alpha": 0.03, "rolling_periods": 300, "center": true}, "fillna": "ffill", "transform_dict": {"fillna": "nearest", "transformations": {"0": null}, "transformation_params": {"0": {}}}, "isolated_only": false}, "remove_excess_anomalies": true, "impact": "datepart_regression", "regression_params": {"regression_model": {"model": "ElasticNet", "model_params": {"l1_ratio": 0.1, "fit_intercept": true, "selection": "cyclic"}}, "datepart_method": "simple", "polynomial_degree": null, "transform_dict": null, "holiday_countries_used": false}}, "1": {}}}',
"Ensemble": 0,
},
"77": { # optimized on M5, 60 SMAPE 2024-06-06
'Model': 'BallTreeMultivariateMotif',
'ModelParameters': '{"window": 28, "point_method": "median", "distance_metric": "euclidean", "k": 15}',
'TransformationParameters': '{"fillna": "ffill_mean_biased", "transformations": {"0": "QuantileTransformer", "1": "QuantileTransformer", "2": "DatepartRegression"}, "transformation_params": {"0": {"output_distribution": "uniform", "n_quantiles": 1000}, "1": {"output_distribution": "normal", "n_quantiles": 100}, "2": {"regression_model": {"model": "ElasticNet", "model_params": {"l1_ratio": 0.1, "fit_intercept": true, "selection": "cyclic"}}, "datepart_method": "expanded", "polynomial_degree": null, "transform_dict": null, "holiday_countries_used": false}}}',
"Ensemble": 0,
},

}

general_template = pd.DataFrame.from_dict(general_template_dict, orient='index')
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