diff --git a/LICENSE b/LICENSE index 16c3df33..7e46a03e 100644 --- a/LICENSE +++ b/LICENSE @@ -1,6 +1,6 @@ MIT License -Copyright (c) 2022 Colin Catlin +Copyright (c) 2024 Colin Catlin Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal diff --git a/TODO.md b/TODO.md index bf1e9d2e..04404fbd 100644 --- a/TODO.md +++ b/TODO.md @@ -12,10 +12,9 @@ * The most recent data will generally be the most important * Forecasts are desired for the future immediately following the most recent data. -# 0.6.8 🇺🇦 🇺🇦 🇺🇦 -* bug fixes, robust for OpenBLAS nan handling kernel failures -* added BKBandpassFilter -* added expand_horizontal for scaling mosaics +# 0.6.9 🇺🇦 🇺🇦 🇺🇦 +* expanded regressor options for MultivariateRegression, NeuralForecast (currently only available directly, not from AutoTS class) +* matse bug fix on all 0 history ### Unstable Upstream Pacakges (those that are frequently broken by maintainers) * Pytorch-Forecasting diff --git a/autots/__init__.py b/autots/__init__.py index 94cc7289..de6e8eec 100644 --- a/autots/__init__.py +++ b/autots/__init__.py @@ -26,7 +26,7 @@ from autots.models.cassandra import Cassandra -__version__ = '0.6.8' +__version__ = '0.6.9' TransformTS = GeneralTransformer diff --git a/autots/evaluator/anomaly_detector.py b/autots/evaluator/anomaly_detector.py index 07e6f668..5f698809 100644 --- a/autots/evaluator/anomaly_detector.py +++ b/autots/evaluator/anomaly_detector.py @@ -91,7 +91,8 @@ def detect(self, df): model = GeneralTransformer( verbose=2, **self.transform_dict ) # DATEPART, LOG, SMOOTHING, DIFF, CLIP OUTLIERS with high z score - self.df_anomaly = model.fit_transform(self.df_anomaly) + # the post selecting by columns is for CenterSplit and any similar renames or expansions + self.df_anomaly = model.fit_transform(self.df_anomaly)[self.df.columns] if self.forecast_params is not None: backcast = back_forecast( diff --git a/autots/evaluator/auto_model.py b/autots/evaluator/auto_model.py index 054212ac..aea10eb8 100644 --- a/autots/evaluator/auto_model.py +++ b/autots/evaluator/auto_model.py @@ -982,6 +982,7 @@ def __init__( per_series_uwmse=None, per_series_smoothness=None, per_series_mate=None, + per_series_matse=None, per_series_wasserstein=None, per_series_dwd=None, model_count: int = 0, @@ -1005,6 +1006,7 @@ def __init__( self.per_series_uwmse = per_series_uwmse self.per_series_smoothness = per_series_smoothness self.per_series_mate = per_series_mate + self.per_series_matse = per_series_matse self.per_series_wasserstein = per_series_wasserstein self.per_series_dwd = per_series_dwd self.full_mae_ids = [] @@ -1083,6 +1085,9 @@ def concat(self, another_eval): self.per_series_mate = pd.concat( [self.per_series_mate, another_eval.per_series_mate], axis=0, sort=False ) + self.per_series_matse = pd.concat( + [self.per_series_matse, another_eval.per_series_matse], axis=0, sort=False + ) self.per_series_wasserstein = pd.concat( [self.per_series_wasserstein, another_eval.per_series_wasserstein], axis=0, @@ -1667,57 +1672,6 @@ def virtual_memory(): ps_metric.index = [model_id] * ps_metric.shape[0] ps_metric.index.name = "ID" template_result.per_series_metrics.append(ps_metric) - - """ - template_result.per_series_mae.append( - _ps_metric(ps_metric, 'mae', model_id) - ) - template_result.per_series_made.append( - _ps_metric(ps_metric, 'made', model_id) - ) - template_result.per_series_contour.append( - _ps_metric(ps_metric, 'contour', model_id) - ) - template_result.per_series_rmse.append( - _ps_metric(ps_metric, 'rmse', model_id) - ) - template_result.per_series_spl.append( - _ps_metric(ps_metric, 'spl', model_id) - ) - template_result.per_series_mle.append( - _ps_metric(ps_metric, 'mle', model_id) - ) - template_result.per_series_imle.append( - _ps_metric(ps_metric, 'imle', model_id) - ) - template_result.per_series_maxe.append( - _ps_metric(ps_metric, 'maxe', model_id) - ) - template_result.per_series_oda.append( - _ps_metric(ps_metric, 'oda', model_id) - ) - template_result.per_series_mqae.append( - _ps_metric(ps_metric, 'mqae', model_id) - ) - template_result.per_series_dwae.append( - _ps_metric(ps_metric, 'dwae', model_id) - ) - template_result.per_series_ewmae.append( - _ps_metric(ps_metric, 'ewmae', model_id) - ) - template_result.per_series_uwmse.append( - _ps_metric(ps_metric, 'uwmse', model_id) - ) - template_result.per_series_smoothness.append( - _ps_metric(ps_metric, 'smoothness', model_id) - ) - template_result.per_series_mate.append( - _ps_metric(ps_metric, 'mate', model_id) - ) - template_result.per_series_wasserstein.append( - _ps_metric(ps_metric, 'wasserstein', model_id) - ) - """ if 'distance' in ensemble: cur_smape = model_error.per_timestamp.loc['weighted_smape'] cur_smape = pd.DataFrame(cur_smape).transpose() @@ -1864,62 +1818,15 @@ def virtual_memory(): template_result.per_series_mate = ps[ps['autots_eval_metric'] == 'mate'].drop( columns='autots_eval_metric' ) + template_result.per_series_matse = ps[ps['autots_eval_metric'] == 'matse'].drop( + columns='autots_eval_metric' + ) template_result.per_series_wasserstein = ps[ ps['autots_eval_metric'] == 'wasserstein' ].drop(columns='autots_eval_metric') template_result.per_series_dwd = ps[ps['autots_eval_metric'] == 'dwd'].drop( columns='autots_eval_metric' ) - """ - template_result.per_series_mae = pd.concat( - template_result.per_series_mae, axis=0 - ) - template_result.per_series_made = pd.concat( - template_result.per_series_made, axis=0 - ) - template_result.per_series_contour = pd.concat( - template_result.per_series_contour, axis=0 - ) - template_result.per_series_rmse = pd.concat( - template_result.per_series_rmse, axis=0 - ) - template_result.per_series_spl = pd.concat( - template_result.per_series_spl, axis=0 - ) - template_result.per_series_mle = pd.concat( - template_result.per_series_mle, axis=0 - ) - template_result.per_series_imle = pd.concat( - template_result.per_series_imle, axis=0 - ) - template_result.per_series_maxe = pd.concat( - template_result.per_series_maxe, axis=0 - ) - template_result.per_series_oda = pd.concat( - template_result.per_series_oda, axis=0 - ) - template_result.per_series_mqae = pd.concat( - template_result.per_series_mqae, axis=0 - ) - template_result.per_series_dwae = pd.concat( - template_result.per_series_dwae, axis=0 - ) - template_result.per_series_ewmae = pd.concat( - template_result.per_series_ewmae, axis=0 - ) - template_result.per_series_uwmse = pd.concat( - template_result.per_series_uwmse, axis=0 - ) - template_result.per_series_smoothness = pd.concat( - template_result.per_series_smoothness, axis=0 - ) - template_result.per_series_mate = pd.concat( - template_result.per_series_mate, axis=0 - ) - template_result.per_series_wasserstein = pd.concat( - template_result.per_series_wasserstein, axis=0 - ) - """ else: template_result.per_series_metrics = pd.DataFrame() template_result.per_series_mae = pd.DataFrame() @@ -1937,6 +1844,7 @@ def virtual_memory(): template_result.per_series_uwmse = pd.DataFrame() template_result.per_series_smoothness = pd.DataFrame() template_result.per_series_mate = pd.DataFrame() + template_result.per_series_matse = pd.DataFrame() template_result.per_series_wasserstein = pd.DataFrame() template_result.per_series_dwd = pd.DataFrame() if verbose > 0 and not template.empty: @@ -2731,6 +2639,7 @@ def generate_score_per_series( uwmse_weighting = metric_weighting.get('uwmse_weighting', 0) smoothness_weighting = metric_weighting.get('smoothness_weighting', 0) mate_weighting = metric_weighting.get('mate_weighting', 0) + matse_weighting = metric_weighting.get('matse_weighting', 0) wasserstein_weighting = metric_weighting.get('wasserstein_weighting', 0) dwd_weighting = metric_weighting.get('dwd_weighting', 0) @@ -2823,6 +2732,14 @@ def generate_score_per_series( ) mate_score = results_object.per_series_mate / mate_scaler overall_score = overall_score + (mate_score * mate_weighting) + if matse_weighting != 0: + matse_scaler = ( + results_object.per_series_matse[results_object.per_series_matse != 0] + .min() + .fillna(1) + ) + matse_score = results_object.per_series_matse / matse_scaler + overall_score = overall_score + (matse_score * matse_weighting) if wasserstein_weighting != 0: wasserstein_scaler = ( results_object.per_series_wasserstein[ diff --git a/autots/evaluator/auto_ts.py b/autots/evaluator/auto_ts.py index 5a4098c4..0fa41495 100644 --- a/autots/evaluator/auto_ts.py +++ b/autots/evaluator/auto_ts.py @@ -1393,7 +1393,7 @@ def fit( validation_template = validation_template.drop_duplicates( subset=['Model', 'ModelParameters', 'TransformationParameters'] ) - self.validation_template = validation_template[self.template_cols] + self.validation_template = validation_template[self.template_cols_id] if self.validate_import is not None: self.validation_template = pd.concat( [self.validation_template, self.validate_import] @@ -1813,7 +1813,14 @@ def validation_agg(self): ) return self - def _set_best_model(self, metric_weighting=None, allow_horizontal=True): + def _set_best_model(self, metric_weighting=None, allow_horizontal=True, n=1): + """Sets best model based on validation results. + + Args: + metric_weighting (dict): if not None, overrides input metric weighting this this metric weighting + allow_horizontal (bool): if False, force no horizontal, if True, allows if ensemble param and runs occurred + n (int): default 1 means chose best model, 2 = use 2nd best, and so on + """ if metric_weighting is None: metric_weighting = self.metric_weighting hens_model_results = self.initial_results.model_results[ @@ -1828,7 +1835,7 @@ def _set_best_model(self, metric_weighting=None, allow_horizontal=True): # horizontal ensembles can't be compared directly to others because they don't get run through all validations # they are built themselves from cross validation so a full rerun of validations is unnecessary self.best_model_non_horizontal = self._best_non_horizontal( - metric_weighting=metric_weighting + metric_weighting=metric_weighting, n=n ) if not hens_model_results.empty and requested_H_ens: hens_model_results.loc['Score'] = generate_score( @@ -1838,7 +1845,7 @@ def _set_best_model(self, metric_weighting=None, allow_horizontal=True): ) self.best_model = hens_model_results.sort_values( by="Score", ascending=True, na_position='last' - ).head(1)[self.template_cols_id] + ).iloc[(n - 1) : n][self.template_cols_id] self.ensemble_check = 1 # print a warning if requested but unable to produce a horz ensemble elif requested_H_ens: @@ -1859,7 +1866,7 @@ def _set_best_model(self, metric_weighting=None, allow_horizontal=True): self.parse_best_model() return self - def _best_non_horizontal(self, metric_weighting=None, series=None): + def _best_non_horizontal(self, metric_weighting=None, series=None, n=1): if self.validation_results is None: if not self.initial_results.model_results.empty: self = self.validation_agg() @@ -1908,7 +1915,7 @@ def _best_non_horizontal(self, metric_weighting=None, series=None): by="Score", ascending=True, na_position='last' ) .drop_duplicates(subset=self.template_cols) - .head(1)[self.template_cols_id] + .iloc[(n - 1) : n][self.template_cols_id] ) except IndexError: raise ValueError( diff --git a/autots/evaluator/metrics.py b/autots/evaluator/metrics.py index 271cc266..44c3e73e 100644 --- a/autots/evaluator/metrics.py +++ b/autots/evaluator/metrics.py @@ -682,7 +682,9 @@ def full_metric_evaluation( else: mate = np.abs(np.sum(full_errors, axis=0)) # possibly temporary - matse = mate / np.sum(A, axis=0) + matse_scale = np.sum(A, axis=0) + matse_scale[matse_scale == 0] = 1 + matse = mate / matse_scale direc_sign = np.sign(F - last_of_array) == np.sign(A - last_of_array) weights = np.geomspace(1, 10, full_mae_errors.shape[0])[:, np.newaxis] diff --git a/autots/models/cassandra.py b/autots/models/cassandra.py index cc843e7e..30481520 100644 --- a/autots/models/cassandra.py +++ b/autots/models/cassandra.py @@ -1138,7 +1138,7 @@ def process_components(self, to_origin_space=True): pd.DataFrame( self.components[:, comp, :], index=t_indx, - columns=self.column_names, + columns=self.df.columns, ), components=True, bounds=True, @@ -2537,7 +2537,7 @@ def lstsq_minimize(X, y, maxiter=15000, cost_function="l1", method=None): elif cost_function == "quantile": cost_func = cost_function_quantile elif cost_function == "l1_positive": - bounds = [(0, 10) for x in x0] + bounds = [(0, 14) for x in x0] cost_func = cost_function_l1 else: cost_func = cost_function_l1 diff --git a/autots/models/ensemble.py b/autots/models/ensemble.py index 4e9ae449..d8a64efe 100644 --- a/autots/models/ensemble.py +++ b/autots/models/ensemble.py @@ -400,6 +400,8 @@ def horizontal_classifier( if classifier_params is None: # found using FLAML classifier_params = {"model": 'KNN', "model_params": {'n_neighbors': 5}} + # newer, but don't like as much + # RandomForest {'n_estimators': 69, 'max_features': 0.5418860350847585, 'max_leaves': 439, 'criterion': 'gini'} # known = {'EXUSEU': 'xx1', 'MCOILWTICO': 'xx2', 'CSUSHPISA': 'xx3'} Xt, Y, Xf = horizontal_xy(df_train, known) @@ -473,6 +475,16 @@ def mosaic_classifier(df_train, known, classifier_params=None): 'criterion': 'gini', }, } + # slightly newer, on a mosaic-weighted-0-40 + classifier_params = { + "model": 'ExtraTrees', + "model_params": { + 'n_estimators': 62, + 'max_features': 0.181116, + 'max_leaves': 261, + 'criterion': 'entropy', + }, + } X, Xf, Y, to_predict = mosaic_xy(df_train, known) diff --git a/autots/models/neural_forecast.py b/autots/models/neural_forecast.py index 02fe6ce2..308ce732 100644 --- a/autots/models/neural_forecast.py +++ b/autots/models/neural_forecast.py @@ -68,19 +68,24 @@ def __init__( self.model_args = model_args self.forecast_length = forecast_length self.df_train = None + self.static_regressor = None - def fit(self, df, future_regressor=None): + def fit(self, df, future_regressor=None, static_regressor=None): """Train algorithm given data supplied. Args: df (pandas.DataFrame): Datetime Indexed """ self.basic_profile(df) - if self.regression_type in ["User", "user"]: + static_cols = None + if self.regression_type in ["User", "user", True]: if future_regressor is None: raise ValueError( "regression_type='User' but no future_regressor passed" ) + self.static_regressor = static_regressor + if isinstance(self.static_regressor, pd.DataFrame): + static_cols = static_regressor.columns.tolist() from neuralforecast import NeuralForecast from neuralforecast.losses.pytorch import ( @@ -181,8 +186,9 @@ def fit(self, df, future_regressor=None): } models = self.model model_args = self.model_args - if self.regression_type in ['User', 'user']: + if self.regression_type in ['User', 'user', True]: self.base_args["futr_exog_list"] = future_regressor.columns.tolist() + self.base_args['stat_exog_list'] = static_cols if isinstance(models, list): # User inputs classes directly @@ -211,12 +217,19 @@ def fit(self, df, future_regressor=None): silly_format = df.reset_index(names='ds').melt( id_vars='ds', value_name='y', var_name='unique_id' ) - if self.regression_type in ['User', 'user']: + if self.regression_type in ['User', 'user', True]: silly_format = silly_format.merge( future_regressor, left_on='ds', right_index=True ) self.nf = NeuralForecast(models=models, freq=freq) - self.nf.fit(df=silly_format) + if self.static_regressor is None: + self.nf.fit(df=silly_format) + else: + another_silly_format = self.static_regressor + another_silly_format.index.name = "unique_id" + self.nf.fit( + df=silly_format, static_df=another_silly_format.reset_index(drop=False) + ) self.fit_runtime = datetime.datetime.now() - self.startTime return self @@ -224,7 +237,7 @@ def predict( self, forecast_length=None, future_regressor=None, just_point_forecast=False ): predictStartTime = datetime.datetime.now() - if self.regression_type in ['User', 'user']: + if self.regression_type in ['User', 'user', True]: index = self.create_forecast_index(forecast_length=self.forecast_length) futr_df = pd.concat( [ diff --git a/autots/models/sklearn.py b/autots/models/sklearn.py index 06ab7d3d..55629844 100644 --- a/autots/models/sklearn.py +++ b/autots/models/sklearn.py @@ -3,6 +3,7 @@ Decision Tree, Elastic Net, Random Forest, MLPRegressor, KNN, Adaboost """ +import hashlib import datetime import random import numpy as np @@ -74,9 +75,11 @@ def rolling_x_regressor( X = [local_df.rename(columns=lambda x: "lastvalue_" + x)] if str(mean_rolling_periods).isdigit(): temp = local_df.rolling(int(mean_rolling_periods), min_periods=1).median() + # temp.columns = ['rollingmean' for col in temp.columns] X.append(temp) if str(macd_periods).isdigit(): temp = local_df.rolling(int(macd_periods), min_periods=1).median() - temp + temp.columns = ['macd' for col in temp.columns] X.append(temp) if str(std_rolling_periods).isdigit(): X.append(local_df.rolling(std_rolling_periods, min_periods=1).std()) @@ -93,7 +96,9 @@ def rolling_x_regressor( local_df.rolling(quantile10_rolling_periods, min_periods=1).quantile(0.1) ) if str(ewm_alpha).replace('.', '').isdigit(): - X.append(local_df.ewm(alpha=ewm_alpha, ignore_na=True, min_periods=1).mean()) + ewm_df = local_df.ewm(alpha=ewm_alpha, ignore_na=True, min_periods=1).mean() + ewm_df.columns = ["ewm_alpha" for col in local_df.columns] + X.append(ewm_df) if str(ewm_var_alpha).replace('.', '').isdigit(): X.append(local_df.ewm(alpha=ewm_var_alpha, ignore_na=True, min_periods=1).var()) if str(additional_lag_periods).isdigit(): @@ -148,6 +153,7 @@ def rolling_x_regressor( temp = local_df.rolling(rolling_autocorr_periods).apply( lambda x: x.autocorr(), raw=False ) + temp.columns = ['rollautocorr' for col in temp.columns] X.append(temp) # unlike the others, this pulls the entire window, not just one lag if str(window).isdigit(): @@ -214,8 +220,11 @@ def rolling_x_regressor_regressor( polynomial_degree: int = None, window: int = None, future_regressor=None, + regressor_per_series=None, + static_regressor=None, cointegration: str = None, cointegration_lag: int = 1, + series_id=None, ): """Adds in the future_regressor.""" X = rolling_x_regressor( @@ -243,6 +252,18 @@ def rolling_x_regressor_regressor( ) if future_regressor is not None: X = pd.concat([X, future_regressor], axis=1) + if regressor_per_series is not None: + X = X.merge(regressor_per_series, left_index=True, right_index=True, how='left') + if static_regressor is not None: + X['series_id'] = df.columns[0] + X = X.merge(static_regressor, left_on="series_id", right_index=True, how='left') + X = X.drop(columns=['series_id']) + if series_id is not None: + hashed = ( + int(hashlib.sha256(str(series_id).encode('utf-8')).hexdigest(), 16) + % 10**16 + ) + X['series_id'] = hashed return X @@ -790,7 +811,7 @@ def generate_classifier_params( if model_dict is None: if method == "fast": model_dict = { - 'xgboost': 1, + 'xgboost': 0.5, # also crashes sometimes # 'ExtraTrees': 0.2, # crashes sometimes # 'RandomForest': 0.1, 'KNN': 1, @@ -1727,8 +1748,9 @@ def __init__( self.shuffle = shuffle self.forecast_length = forecast_length self.max_windows = max_windows + self.static_regressor = None - def fit(self, df, future_regressor=None): + def fit(self, df, future_regressor=None, static_regressor=None): """Train algorithm given data supplied. Args: @@ -1746,6 +1768,7 @@ def fit(self, df, future_regressor=None): raise ValueError( "regression_type='User' but no future_regressor passed" ) + self.static_regressor = static_regressor self.df_train = df if isinstance(future_regressor, pd.Series): future_regressor = future_regressor.to_frame() @@ -2250,7 +2273,13 @@ def __init__( self.polynomial_degree = polynomial_degree self.forecast_length = forecast_length - def fit(self, df, future_regressor=None): + def fit( + self, + df, + future_regressor=None, + static_regressor=None, + regressor_per_series=None, + ): """Train algorithm given data supplied. Args: @@ -2306,6 +2335,7 @@ def predict( future_regressor=None, just_point_forecast: bool = False, df=None, + regressor_per_series=None, ): """Generate forecast data immediately following dates of index supplied to .fit(). @@ -2918,6 +2948,7 @@ def __init__( }, cointegration: str = None, cointegration_lag: int = 1, + series_hash: bool = False, n_jobs: int = -1, **kwargs, ): @@ -2956,10 +2987,13 @@ def __init__( self.window = window self.quantile_params = quantile_params self.regressor_train = None + self.regressor_per_series_train = None + self.static_regressor = None self.probabilistic = probabilistic self.scale_full_X = scale_full_X self.cointegration = cointegration self.cointegration_lag = cointegration_lag + self.series_hash = series_hash # detect just the max needed for cutoff (makes faster) starting_min = 90 # based on what effects ewm alphas, too @@ -2983,6 +3017,7 @@ def __init__( def base_scaler(self, df): self.scaler_mean = np.mean(df, axis=0) self.scaler_std = np.std(df, axis=0) + self.scaler_std[self.scaler_std == 0] = 1 return (df - self.scaler_mean) / self.scaler_std def scale_data(self, df): @@ -2997,7 +3032,13 @@ def to_origin_space( """Take transformed outputs back to original feature space.""" return df * self.scaler_std + self.scaler_mean - def fit(self, df, future_regressor=None): + def fit( + self, + df, + future_regressor=None, + static_regressor=None, + regressor_per_series=None, + ): """Train algorithm given data supplied. Args: @@ -3013,12 +3054,17 @@ def fit(self, df, future_regressor=None): raise ValueError( "regression_type='User' but not future_regressor supplied." ) - elif future_regressor.shape[0] != df.shape[0]: - raise ValueError( - "future_regressor shape does not match training data shape." - ) + # broken by slice + # elif future_regressor.shape[0] != df.shape[0]: + # raise ValueError( + # "future_regressor shape does not match training data shape." + # ) else: - self.regressor_train = future_regressor + self.regressor_train = future_regressor.reindex(df.index) + if regressor_per_series is not None: + self.regressor_per_series_train = regressor_per_series + if static_regressor is not None: + self.static_regressor = static_regressor # define X and Y self.Y = df[1:].to_numpy().ravel(order="F") # drop look ahead data @@ -3052,8 +3098,16 @@ def fit(self, df, future_regressor=None): polynomial_degree=self.polynomial_degree, window=self.window, future_regressor=cut_regr, + # these rely the if part not being run if None + regressor_per_series=self.regressor_per_series_train[x_col] + if self.regressor_per_series_train is not None + else None, + static_regressor=static_regressor.loc[x_col].to_frame().T + if self.static_regressor is not None + else None, cointegration=self.cointegration, cointegration_lag=self.cointegration_lag, + series_id=x_col if self.series_hash else None, ) for x_col in base.columns ] @@ -3093,6 +3147,7 @@ def fit(self, df, future_regressor=None): if self.scale_full_X: self.X = self.scale_data(self.X) + # Remember the X datetime is for the previous day to the Y datetime here self.model.fit(self.X.to_numpy(), self.Y) if self.probabilistic and not self.multioutputgpr: @@ -3105,11 +3160,22 @@ def fit(self, df, future_regressor=None): self.fit_runtime = datetime.datetime.now() - self.startTime return self - def fit_data(self, df, future_regressor=None): + def fit_data( + self, + df, + future_regressor=None, + static_regressor=None, + regressor_per_series=None, + ): df = self.basic_profile(df) self.sktraindata = df.tail(self.min_threshold) - if future_regressor is not None: - self.regressor_train = future_regressor + if self.regression_type is not None: + if future_regressor is not None: + self.regressor_train = future_regressor.reindex(df) + if regressor_per_series is not None: + self.regressor_per_series_train = regressor_per_series + if static_regressor is not None: + self.static_regressor = static_regressor return self def predict( @@ -3118,6 +3184,7 @@ def predict( just_point_forecast: bool = False, future_regressor=None, df=None, + regressor_per_series=None, ): """Generate forecast data immediately following dates of index supplied to .fit(). @@ -3177,8 +3244,16 @@ def predict( polynomial_degree=self.polynomial_degree, window=self.window, future_regressor=cur_regr, + # these rely the if part not being run if None + regressor_per_series=regressor_per_series[x_col] + if self.regressor_per_series_train is not None + else None, + static_regressor=self.static_regressor.loc[x_col].to_frame().T + if self.static_regressor is not None + else None, cointegration=self.cointegration, cointegration_lag=self.cointegration_lag, + series_id=x_col if self.series_hash else None, ).tail(1) for x_col in current_x.columns ] @@ -3324,8 +3399,9 @@ def get_new_params(self, method: str = 'random'): "common_fourier", "expanded_binarized", "common_fourier_rw", + ["dayofweek", 365.25], ], - [0.2, 0.05, 0.1, 0.1, 0.05, 0.1, 0.05, 0.05, 0.05, 0.025], + [0.2, 0.1, 0.025, 0.1, 0.05, 0.1, 0.05, 0.05, 0.05, 0.025, 0.05], )[0] holiday_choice = random.choices([True, False], [0.1, 0.9])[0] polynomial_degree_choice = random.choices([None, 2], [0.995, 0.005])[0] @@ -3361,6 +3437,7 @@ def get_new_params(self, method: str = 'random'): 'scale_full_X': random.choices([True, False], [0.2, 0.8])[0], "cointegration": coint_choice, "cointegration_lag": coint_lag, + "series_hash": random.choices([True, False], [0.5, 0.5])[0], } return parameter_dict @@ -3390,6 +3467,7 @@ def get_params(self): 'scale_full_X': self.scale_full_X, "cointegration": self.cointegration, "cointegration_lag": self.cointegration_lag, + "series_hash": self.series_hash, } return parameter_dict diff --git a/autots/tools/transform.py b/autots/tools/transform.py index d148d75d..9de1bf5a 100644 --- a/autots/tools/transform.py +++ b/autots/tools/transform.py @@ -5480,6 +5480,7 @@ def transformer_list_to_dict(transformer_list): # temporary removal for testing del transformer_list["RegressionFilter"] del transformer_list["LocalLinearTrend"] + del transformer_list["ReplaceConstant"] if isinstance(transformer_list, dict): transformer_prob = list(transformer_list.values()) diff --git a/docs/build/doctrees/environment.pickle b/docs/build/doctrees/environment.pickle index 3fb987c7..2536b08c 100644 Binary files a/docs/build/doctrees/environment.pickle and b/docs/build/doctrees/environment.pickle differ diff --git a/docs/build/doctrees/source/autots.evaluator.doctree b/docs/build/doctrees/source/autots.evaluator.doctree index 718e3cc8..46a5354c 100644 Binary files a/docs/build/doctrees/source/autots.evaluator.doctree and b/docs/build/doctrees/source/autots.evaluator.doctree differ diff --git a/docs/build/doctrees/source/autots.models.doctree b/docs/build/doctrees/source/autots.models.doctree index 02d9abdc..663e2c58 100644 Binary files a/docs/build/doctrees/source/autots.models.doctree and b/docs/build/doctrees/source/autots.models.doctree differ diff --git a/docs/build/html/searchindex.js b/docs/build/html/searchindex.js index f95b08dd..eec17670 100644 --- a/docs/build/html/searchindex.js +++ b/docs/build/html/searchindex.js @@ -1 +1 @@ -Search.setIndex({"docnames": ["index", "source/autots", "source/autots.datasets", "source/autots.evaluator", "source/autots.models", "source/autots.templates", "source/autots.tools", "source/intro", "source/modules", "source/tutorial"], "filenames": ["index.rst", "source\\autots.rst", 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(autots.evaluator.auto_ts.autots.initial_results attribute)": [[3, "autots.evaluator.auto_ts.AutoTS.initial_results.model_results"]], "mosaic_to_df() (autots.evaluator.auto_ts.autots method)": [[3, "autots.evaluator.auto_ts.AutoTS.mosaic_to_df"]], "mqae() (in module autots.evaluator.metrics)": [[3, "autots.evaluator.metrics.mqae"]], "msle() (in module autots.evaluator.metrics)": [[3, "autots.evaluator.metrics.msle"]], "numpy_ffill() (in module autots.evaluator.metrics)": [[3, "autots.evaluator.metrics.numpy_ffill"]], "oda() (in module autots.evaluator.metrics)": [[3, "autots.evaluator.metrics.oda"]], "parse_best_model() (autots.evaluator.auto_ts.autots method)": [[3, "autots.evaluator.auto_ts.AutoTS.parse_best_model"]], "pinball_loss() (in module autots.evaluator.metrics)": [[3, "autots.evaluator.metrics.pinball_loss"]], "plot() (autots.evaluator.anomaly_detector.anomalydetector method)": [[3, "autots.evaluator.anomaly_detector.AnomalyDetector.plot"]], "plot() (autots.evaluator.anomaly_detector.holidaydetector method)": [[3, "autots.evaluator.anomaly_detector.HolidayDetector.plot"]], "plot() (autots.evaluator.event_forecasting.eventriskforecast method)": [[3, "autots.evaluator.event_forecasting.EventRiskForecast.plot"], [3, "id10"]], "plot_anomaly() (autots.evaluator.anomaly_detector.holidaydetector method)": [[3, "autots.evaluator.anomaly_detector.HolidayDetector.plot_anomaly"]], "plot_back_forecast() (autots.evaluator.auto_ts.autots method)": [[3, "autots.evaluator.auto_ts.AutoTS.plot_back_forecast"]], "plot_backforecast() (autots.evaluator.auto_ts.autots method)": [[3, "autots.evaluator.auto_ts.AutoTS.plot_backforecast"]], "plot_eval() (autots.evaluator.event_forecasting.eventriskforecast method)": [[3, "autots.evaluator.event_forecasting.EventRiskForecast.plot_eval"]], "plot_generation_loss() (autots.evaluator.auto_ts.autots method)": [[3, "autots.evaluator.auto_ts.AutoTS.plot_generation_loss"]], "plot_horizontal() (autots.evaluator.auto_ts.autots method)": [[3, "autots.evaluator.auto_ts.AutoTS.plot_horizontal"]], "plot_horizontal_model_count() (autots.evaluator.auto_ts.autots method)": [[3, "autots.evaluator.auto_ts.AutoTS.plot_horizontal_model_count"]], "plot_horizontal_per_generation() (autots.evaluator.auto_ts.autots method)": [[3, "autots.evaluator.auto_ts.AutoTS.plot_horizontal_per_generation"]], "plot_horizontal_transformers() (autots.evaluator.auto_ts.autots method)": [[3, "autots.evaluator.auto_ts.AutoTS.plot_horizontal_transformers"]], "plot_metric_corr() (autots.evaluator.auto_ts.autots method)": [[3, "autots.evaluator.auto_ts.AutoTS.plot_metric_corr"]], "plot_per_series_error() (autots.evaluator.auto_ts.autots method)": [[3, "autots.evaluator.auto_ts.AutoTS.plot_per_series_error"]], "plot_per_series_mape() (autots.evaluator.auto_ts.autots method)": [[3, "autots.evaluator.auto_ts.AutoTS.plot_per_series_mape"]], "plot_per_series_smape() (autots.evaluator.auto_ts.autots method)": [[3, "autots.evaluator.auto_ts.AutoTS.plot_per_series_smape"]], "plot_transformer_failure_rate() (autots.evaluator.auto_ts.autots method)": [[3, "autots.evaluator.auto_ts.AutoTS.plot_transformer_failure_rate"]], "plot_validations() (autots.evaluator.auto_ts.autots method)": [[3, "autots.evaluator.auto_ts.AutoTS.plot_validations"]], "precomp_wasserstein() (in module autots.evaluator.metrics)": [[3, "autots.evaluator.metrics.precomp_wasserstein"]], "predict() (autots.evaluator.auto_model.modelprediction method)": [[3, "autots.evaluator.auto_model.ModelPrediction.predict"]], "predict() (autots.evaluator.auto_ts.autots method)": [[3, "autots.evaluator.auto_ts.AutoTS.predict"]], "predict() (autots.evaluator.event_forecasting.eventriskforecast method)": [[3, "autots.evaluator.event_forecasting.EventRiskForecast.predict"], [3, "id11"]], "predict_historic() (autots.evaluator.event_forecasting.eventriskforecast method)": [[3, "autots.evaluator.event_forecasting.EventRiskForecast.predict_historic"], [3, "id12"]], "qae() (in module autots.evaluator.metrics)": [[3, "autots.evaluator.metrics.qae"]], "random_model() (in module autots.evaluator.auto_model)": [[3, "autots.evaluator.auto_model.random_model"]], "regression_check (autots.evaluator.auto_ts.autots attribute)": [[3, "autots.evaluator.auto_ts.AutoTS.regression_check"]], "remove_leading_zeros() (in module autots.evaluator.auto_model)": [[3, "autots.evaluator.auto_model.remove_leading_zeros"]], "results() (autots.evaluator.auto_ts.autots method)": [[3, "autots.evaluator.auto_ts.AutoTS.results"]], "retrieve_validation_forecasts() (autots.evaluator.auto_ts.autots method)": [[3, "autots.evaluator.auto_ts.AutoTS.retrieve_validation_forecasts"]], "rmse() (in module autots.evaluator.metrics)": [[3, "autots.evaluator.metrics.rmse"]], "root_mean_square_error() (in module autots.evaluator.metrics)": [[3, "autots.evaluator.metrics.root_mean_square_error"]], "rps() (in module autots.evaluator.metrics)": [[3, "autots.evaluator.metrics.rps"]], "run() (autots.evaluator.benchmark.benchmark method)": [[3, "autots.evaluator.benchmark.Benchmark.run"]], "save() (autots.evaluator.auto_model.templateevalobject method)": [[3, "autots.evaluator.auto_model.TemplateEvalObject.save"]], "save_template() (autots.evaluator.auto_ts.autots method)": [[3, "autots.evaluator.auto_ts.AutoTS.save_template"]], "scaled_pinball_loss() (in module autots.evaluator.metrics)": [[3, "autots.evaluator.metrics.scaled_pinball_loss"]], "score_per_series (autots.evaluator.auto_ts.autots attribute)": [[3, "autots.evaluator.auto_ts.AutoTS.score_per_series"]], "score_to_anomaly() (autots.evaluator.anomaly_detector.anomalydetector method)": [[3, "autots.evaluator.anomaly_detector.AnomalyDetector.score_to_anomaly"]], "set_limit() (autots.evaluator.event_forecasting.eventriskforecast method)": [[3, "autots.evaluator.event_forecasting.EventRiskForecast.set_limit"]], "set_limit() (autots.evaluator.event_forecasting.eventriskforecast static method)": [[3, "id13"]], "set_limit_forecast() (in module autots.evaluator.event_forecasting)": [[3, "autots.evaluator.event_forecasting.set_limit_forecast"]], "set_limit_forecast_historic() (in module autots.evaluator.event_forecasting)": [[3, "autots.evaluator.event_forecasting.set_limit_forecast_historic"]], "smape() (in module autots.evaluator.metrics)": [[3, "autots.evaluator.metrics.smape"]], "smoothness() (in module autots.evaluator.metrics)": [[3, "autots.evaluator.metrics.smoothness"]], "spl() (in module autots.evaluator.metrics)": [[3, "autots.evaluator.metrics.spl"]], "symmetric_mean_absolute_percentage_error() (in module autots.evaluator.metrics)": [[3, "autots.evaluator.metrics.symmetric_mean_absolute_percentage_error"]], "threshold_loss() (in module autots.evaluator.metrics)": [[3, "autots.evaluator.metrics.threshold_loss"]], "trans_dict_recomb() (in module autots.evaluator.auto_model)": [[3, "autots.evaluator.auto_model.trans_dict_recomb"]], "unpack_ensemble_models() (in module autots.evaluator.auto_model)": [[3, "autots.evaluator.auto_model.unpack_ensemble_models"]], "unsorted_wasserstein() (in module autots.evaluator.metrics)": [[3, "autots.evaluator.metrics.unsorted_wasserstein"]], "validate_num_validations() (in module autots.evaluator.validation)": [[3, "autots.evaluator.validation.validate_num_validations"]], "validation_agg() (autots.evaluator.auto_ts.autots method)": [[3, "autots.evaluator.auto_ts.AutoTS.validation_agg"]], "validation_aggregation() (in module autots.evaluator.auto_model)": [[3, "autots.evaluator.auto_model.validation_aggregation"]], "wasserstein() (in module autots.evaluator.metrics)": [[3, "autots.evaluator.metrics.wasserstein"]], "arch (class in autots.models.arch)": [[4, "autots.models.arch.ARCH"]], "ardl (class in autots.models.statsmodels)": [[4, "autots.models.statsmodels.ARDL"]], "arima (class in autots.models.statsmodels)": [[4, "autots.models.statsmodels.ARIMA"]], "averagevaluenaive (class in autots.models.basics)": [[4, "autots.models.basics.AverageValueNaive"]], "balltreemultivariatemotif (class in autots.models.basics)": [[4, "autots.models.basics.BallTreeMultivariateMotif"]], "bayesianmultioutputregression (class in autots.models.cassandra)": [[4, "autots.models.cassandra.BayesianMultiOutputRegression"]], "bestnensemble() (in module autots.models.ensemble)": [[4, "autots.models.ensemble.BestNEnsemble"]], "cassandra (class in autots.models.cassandra)": [[4, "autots.models.cassandra.Cassandra"]], "componentanalysis (class in autots.models.sklearn)": [[4, "autots.models.sklearn.ComponentAnalysis"]], "constantnaive (class in autots.models.basics)": [[4, "autots.models.basics.ConstantNaive"]], "datepartregression (class in autots.models.sklearn)": [[4, "autots.models.sklearn.DatepartRegression"]], "distensemble() (in module autots.models.ensemble)": [[4, "autots.models.ensemble.DistEnsemble"]], "dynamicfactor (class in autots.models.statsmodels)": [[4, "autots.models.statsmodels.DynamicFactor"]], "dynamicfactormq (class in autots.models.statsmodels)": [[4, "autots.models.statsmodels.DynamicFactorMQ"]], "ets (class in autots.models.statsmodels)": [[4, "autots.models.statsmodels.ETS"]], "ensembleforecast() (in module autots.models.ensemble)": [[4, "autots.models.ensemble.EnsembleForecast"]], "ensembletemplategenerator() (in module autots.models.ensemble)": [[4, "autots.models.ensemble.EnsembleTemplateGenerator"]], "fbprophet (class in autots.models.prophet)": [[4, "autots.models.prophet.FBProphet"]], "fft (class in autots.models.basics)": [[4, "autots.models.basics.FFT"]], "glm (class in autots.models.statsmodels)": [[4, "autots.models.statsmodels.GLM"]], "gls (class in autots.models.statsmodels)": [[4, "autots.models.statsmodels.GLS"]], "gluonts (class in autots.models.gluonts)": [[4, "autots.models.gluonts.GluonTS"]], "greykite (class in autots.models.greykite)": [[4, "autots.models.greykite.Greykite"]], "hdistensemble() (in module autots.models.ensemble)": [[4, "autots.models.ensemble.HDistEnsemble"]], "horizontalensemble() (in module autots.models.ensemble)": [[4, "autots.models.ensemble.HorizontalEnsemble"]], "horizontaltemplategenerator() (in module autots.models.ensemble)": [[4, "autots.models.ensemble.HorizontalTemplateGenerator"]], "kalmanstatespace (class in autots.models.basics)": [[4, "autots.models.basics.KalmanStateSpace"]], "kerasrnn (class in autots.models.dnn)": [[4, "autots.models.dnn.KerasRNN"]], "latc (class in autots.models.matrix_var)": [[4, "autots.models.matrix_var.LATC"]], "lastvaluenaive (class in autots.models.basics)": [[4, "autots.models.basics.LastValueNaive"]], "mar (class in autots.models.matrix_var)": [[4, "autots.models.matrix_var.MAR"]], "mlensemble (class in autots.models.mlensemble)": [[4, "autots.models.mlensemble.MLEnsemble"]], "metricmotif (class in autots.models.basics)": [[4, "autots.models.basics.MetricMotif"]], "modelobject (class in autots.models.base)": [[4, "autots.models.base.ModelObject"]], "mosaicensemble() (in module autots.models.ensemble)": [[4, "autots.models.ensemble.MosaicEnsemble"]], "motif (class in autots.models.basics)": [[4, "autots.models.basics.Motif"]], "motifsimulation (class in autots.models.basics)": [[4, "autots.models.basics.MotifSimulation"]], "multivariateregression (class in autots.models.sklearn)": [[4, "autots.models.sklearn.MultivariateRegression"]], "nvar (class in autots.models.basics)": [[4, "autots.models.basics.NVAR"]], "neuralforecast (class in autots.models.neural_forecast)": [[4, "autots.models.neural_forecast.NeuralForecast"]], "neuralprophet (class in autots.models.prophet)": [[4, "autots.models.prophet.NeuralProphet"]], "predictionobject (class in autots.models.base)": [[4, "autots.models.base.PredictionObject"]], "preprocessingregression (class in autots.models.sklearn)": [[4, "autots.models.sklearn.PreprocessingRegression"]], "pytorchforecasting (class in autots.models.pytorch)": [[4, "autots.models.pytorch.PytorchForecasting"]], "rrvar (class in autots.models.matrix_var)": [[4, "autots.models.matrix_var.RRVAR"]], "rollingregression (class in autots.models.sklearn)": [[4, "autots.models.sklearn.RollingRegression"]], "seasonalnaive (class in autots.models.basics)": [[4, "autots.models.basics.SeasonalNaive"]], "seasonalitymotif (class in autots.models.basics)": [[4, "autots.models.basics.SeasonalityMotif"]], "sectionalmotif (class in autots.models.basics)": [[4, "autots.models.basics.SectionalMotif"]], "tfpregression (class in autots.models.tfp)": [[4, "autots.models.tfp.TFPRegression"]], "tfpregressor (class in autots.models.tfp)": [[4, "autots.models.tfp.TFPRegressor"]], "tmf (class in autots.models.matrix_var)": [[4, "autots.models.matrix_var.TMF"]], "tensorflowsts (class in autots.models.tfp)": [[4, "autots.models.tfp.TensorflowSTS"]], "theta (class in autots.models.statsmodels)": [[4, "autots.models.statsmodels.Theta"]], "tide (class in autots.models.tide)": [[4, "autots.models.tide.TiDE"]], "timecovariates (class in autots.models.tide)": [[4, "autots.models.tide.TimeCovariates"]], "timeseriesdata (class in autots.models.tide)": [[4, "autots.models.tide.TimeSeriesdata"]], "transformer (class in autots.models.dnn)": [[4, "autots.models.dnn.Transformer"]], "univariateregression (class in autots.models.sklearn)": [[4, "autots.models.sklearn.UnivariateRegression"]], "unobservedcomponents (class in autots.models.statsmodels)": [[4, "autots.models.statsmodels.UnobservedComponents"]], "var (class in autots.models.statsmodels)": [[4, "autots.models.statsmodels.VAR"]], "varmax (class in autots.models.statsmodels)": [[4, "autots.models.statsmodels.VARMAX"]], "vecm (class in autots.models.statsmodels)": [[4, "autots.models.statsmodels.VECM"]], "vectorizedmultioutputgpr (class in autots.models.sklearn)": [[4, "autots.models.sklearn.VectorizedMultiOutputGPR"]], "windowregression (class in autots.models.sklearn)": [[4, "autots.models.sklearn.WindowRegression"]], "zeroesnaive (in module autots.models.basics)": [[4, "autots.models.basics.ZeroesNaive"]], "analyze_trend() (autots.models.cassandra.cassandra method)": [[4, "autots.models.cassandra.Cassandra.analyze_trend"]], "anomalies (autots.models.cassandra.cassandra..anomaly_detector attribute)": [[4, "autots.models.cassandra.Cassandra..anomaly_detector.anomalies"]], "apply_constraints() (autots.models.base.predictionobject method)": [[4, "autots.models.base.PredictionObject.apply_constraints"], [4, "id0"]], "apply_constraints() (in module autots.models.base)": [[4, "autots.models.base.apply_constraints"]], "arima_seek_the_oracle() (in module autots.models.statsmodels)": [[4, "autots.models.statsmodels.arima_seek_the_oracle"]], "auto_fit() (autots.models.cassandra.cassandra method)": [[4, "autots.models.cassandra.Cassandra.auto_fit"]], "auto_model_list() (in module autots.models.model_list)": [[4, "autots.models.model_list.auto_model_list"]], "autots.models": [[4, "module-autots.models"]], "autots.models.arch": [[4, "module-autots.models.arch"]], "autots.models.base": [[4, "module-autots.models.base"]], "autots.models.basics": [[4, "module-autots.models.basics"]], "autots.models.cassandra": [[4, "module-autots.models.cassandra"]], "autots.models.dnn": [[4, "module-autots.models.dnn"]], "autots.models.ensemble": [[4, "module-autots.models.ensemble"]], "autots.models.gluonts": [[4, "module-autots.models.gluonts"]], "autots.models.greykite": [[4, "module-autots.models.greykite"]], "autots.models.matrix_var": [[4, "module-autots.models.matrix_var"]], "autots.models.mlensemble": [[4, "module-autots.models.mlensemble"]], "autots.models.model_list": [[4, "module-autots.models.model_list"]], "autots.models.neural_forecast": [[4, "module-autots.models.neural_forecast"]], "autots.models.prophet": [[4, "module-autots.models.prophet"]], "autots.models.pytorch": [[4, "module-autots.models.pytorch"]], "autots.models.sklearn": [[4, "module-autots.models.sklearn"]], "autots.models.statsmodels": [[4, "module-autots.models.statsmodels"]], "autots.models.tfp": [[4, "module-autots.models.tfp"]], "autots.models.tide": [[4, "module-autots.models.tide"]], "base_scaler() (autots.models.cassandra.cassandra method)": [[4, "autots.models.cassandra.Cassandra.base_scaler"]], "base_scaler() (autots.models.sklearn.multivariateregression method)": [[4, "autots.models.sklearn.MultivariateRegression.base_scaler"]], "basic_profile() (autots.models.base.modelobject method)": [[4, "autots.models.base.ModelObject.basic_profile"]], "calculate_peak_density() (in module autots.models.base)": [[4, "autots.models.base.calculate_peak_density"]], "clean_regressor() (in module autots.models.cassandra)": [[4, "autots.models.cassandra.clean_regressor"]], "compare_actual_components() (autots.models.cassandra.cassandra method)": [[4, "autots.models.cassandra.Cassandra.compare_actual_components"]], "conj_grad_w() (in module autots.models.matrix_var)": [[4, "autots.models.matrix_var.conj_grad_w"]], "conj_grad_x() (in module autots.models.matrix_var)": [[4, "autots.models.matrix_var.conj_grad_x"]], "cost_function() (autots.models.basics.kalmanstatespace method)": [[4, "autots.models.basics.KalmanStateSpace.cost_function"]], "cost_function_dwae() (in module autots.models.cassandra)": [[4, "autots.models.cassandra.cost_function_dwae"]], "cost_function_l1() (in module autots.models.cassandra)": [[4, "autots.models.cassandra.cost_function_l1"]], "cost_function_l1_positive() (in module autots.models.cassandra)": [[4, "autots.models.cassandra.cost_function_l1_positive"]], "cost_function_l2() (in module autots.models.cassandra)": [[4, "autots.models.cassandra.cost_function_l2"]], "cost_function_quantile() (in module autots.models.cassandra)": [[4, "autots.models.cassandra.cost_function_quantile"]], "create_feature() (in module autots.models.mlensemble)": [[4, "autots.models.mlensemble.create_feature"]], "create_forecast_index() (autots.models.base.modelobject method)": [[4, "autots.models.base.ModelObject.create_forecast_index"]], "create_forecast_index() (autots.models.cassandra.cassandra method)": [[4, "autots.models.cassandra.Cassandra.create_forecast_index"]], "create_forecast_index() (in module autots.models.base)": [[4, "autots.models.base.create_forecast_index"]], "create_seaborn_palette_from_cmap() (in module autots.models.base)": [[4, "autots.models.base.create_seaborn_palette_from_cmap"]], "create_t() (autots.models.cassandra.cassandra method)": [[4, "autots.models.cassandra.Cassandra.create_t"]], "create_t() (in module autots.models.cassandra)": [[4, "autots.models.cassandra.create_t"]], "cross_validate() (autots.models.cassandra.cassandra method)": [[4, "autots.models.cassandra.Cassandra.cross_validate"]], "dates_to_holidays() (autots.models.cassandra.cassandra.holiday_detector method)": [[4, "autots.models.cassandra.Cassandra.holiday_detector.dates_to_holidays"]], "dmd() 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(autots.evaluator.event_forecasting.eventriskforecast method)": [[3, "autots.evaluator.event_forecasting.EventRiskForecast.predict"], [3, "id11"]], "predict_historic() (autots.evaluator.event_forecasting.eventriskforecast method)": [[3, "autots.evaluator.event_forecasting.EventRiskForecast.predict_historic"], [3, "id12"]], "qae() (in module autots.evaluator.metrics)": [[3, "autots.evaluator.metrics.qae"]], "random_model() (in module autots.evaluator.auto_model)": [[3, "autots.evaluator.auto_model.random_model"]], "regression_check (autots.evaluator.auto_ts.autots attribute)": [[3, "autots.evaluator.auto_ts.AutoTS.regression_check"]], "remove_leading_zeros() (in module autots.evaluator.auto_model)": [[3, "autots.evaluator.auto_model.remove_leading_zeros"]], "results() (autots.evaluator.auto_ts.autots method)": [[3, "autots.evaluator.auto_ts.AutoTS.results"]], "retrieve_validation_forecasts() (autots.evaluator.auto_ts.autots method)": [[3, "autots.evaluator.auto_ts.AutoTS.retrieve_validation_forecasts"]], "rmse() (in module autots.evaluator.metrics)": [[3, "autots.evaluator.metrics.rmse"]], "root_mean_square_error() (in module autots.evaluator.metrics)": [[3, "autots.evaluator.metrics.root_mean_square_error"]], "rps() (in module autots.evaluator.metrics)": [[3, "autots.evaluator.metrics.rps"]], "run() (autots.evaluator.benchmark.benchmark method)": [[3, "autots.evaluator.benchmark.Benchmark.run"]], "save() (autots.evaluator.auto_model.templateevalobject method)": [[3, "autots.evaluator.auto_model.TemplateEvalObject.save"]], "save_template() (autots.evaluator.auto_ts.autots method)": [[3, "autots.evaluator.auto_ts.AutoTS.save_template"]], "scaled_pinball_loss() (in module autots.evaluator.metrics)": [[3, "autots.evaluator.metrics.scaled_pinball_loss"]], "score_per_series (autots.evaluator.auto_ts.autots attribute)": [[3, "autots.evaluator.auto_ts.AutoTS.score_per_series"]], "score_to_anomaly() (autots.evaluator.anomaly_detector.anomalydetector method)": [[3, "autots.evaluator.anomaly_detector.AnomalyDetector.score_to_anomaly"]], "set_limit() (autots.evaluator.event_forecasting.eventriskforecast method)": [[3, "autots.evaluator.event_forecasting.EventRiskForecast.set_limit"]], "set_limit() (autots.evaluator.event_forecasting.eventriskforecast static method)": [[3, "id13"]], "set_limit_forecast() (in module autots.evaluator.event_forecasting)": [[3, "autots.evaluator.event_forecasting.set_limit_forecast"]], "set_limit_forecast_historic() (in module autots.evaluator.event_forecasting)": [[3, "autots.evaluator.event_forecasting.set_limit_forecast_historic"]], "smape() (in module autots.evaluator.metrics)": [[3, "autots.evaluator.metrics.smape"]], "smoothness() (in module autots.evaluator.metrics)": [[3, "autots.evaluator.metrics.smoothness"]], "spl() (in module autots.evaluator.metrics)": [[3, "autots.evaluator.metrics.spl"]], "symmetric_mean_absolute_percentage_error() (in module autots.evaluator.metrics)": [[3, "autots.evaluator.metrics.symmetric_mean_absolute_percentage_error"]], "threshold_loss() (in module autots.evaluator.metrics)": [[3, "autots.evaluator.metrics.threshold_loss"]], "trans_dict_recomb() (in module autots.evaluator.auto_model)": [[3, "autots.evaluator.auto_model.trans_dict_recomb"]], "unpack_ensemble_models() (in module autots.evaluator.auto_model)": [[3, "autots.evaluator.auto_model.unpack_ensemble_models"]], "unsorted_wasserstein() (in module autots.evaluator.metrics)": [[3, "autots.evaluator.metrics.unsorted_wasserstein"]], "validate_num_validations() (in module autots.evaluator.validation)": [[3, "autots.evaluator.validation.validate_num_validations"]], "validation_agg() (autots.evaluator.auto_ts.autots method)": [[3, "autots.evaluator.auto_ts.AutoTS.validation_agg"]], "validation_aggregation() (in module autots.evaluator.auto_model)": [[3, "autots.evaluator.auto_model.validation_aggregation"]], "wasserstein() (in module autots.evaluator.metrics)": [[3, "autots.evaluator.metrics.wasserstein"]], "arch (class in autots.models.arch)": [[4, "autots.models.arch.ARCH"]], "ardl (class in autots.models.statsmodels)": [[4, "autots.models.statsmodels.ARDL"]], "arima (class in autots.models.statsmodels)": [[4, "autots.models.statsmodels.ARIMA"]], "averagevaluenaive (class in autots.models.basics)": [[4, "autots.models.basics.AverageValueNaive"]], "balltreemultivariatemotif (class in autots.models.basics)": [[4, "autots.models.basics.BallTreeMultivariateMotif"]], "bayesianmultioutputregression (class in autots.models.cassandra)": [[4, "autots.models.cassandra.BayesianMultiOutputRegression"]], "bestnensemble() (in module autots.models.ensemble)": [[4, "autots.models.ensemble.BestNEnsemble"]], "cassandra (class in autots.models.cassandra)": [[4, "autots.models.cassandra.Cassandra"]], "componentanalysis (class in autots.models.sklearn)": [[4, "autots.models.sklearn.ComponentAnalysis"]], "constantnaive (class in autots.models.basics)": [[4, "autots.models.basics.ConstantNaive"]], "datepartregression (class in autots.models.sklearn)": [[4, "autots.models.sklearn.DatepartRegression"]], "distensemble() (in module autots.models.ensemble)": [[4, "autots.models.ensemble.DistEnsemble"]], "dynamicfactor (class in autots.models.statsmodels)": [[4, "autots.models.statsmodels.DynamicFactor"]], "dynamicfactormq (class in autots.models.statsmodels)": [[4, "autots.models.statsmodels.DynamicFactorMQ"]], "ets (class in autots.models.statsmodels)": [[4, "autots.models.statsmodels.ETS"]], "ensembleforecast() (in module autots.models.ensemble)": [[4, "autots.models.ensemble.EnsembleForecast"]], "ensembletemplategenerator() (in module autots.models.ensemble)": [[4, "autots.models.ensemble.EnsembleTemplateGenerator"]], "fbprophet (class in autots.models.prophet)": [[4, "autots.models.prophet.FBProphet"]], "fft (class in autots.models.basics)": [[4, "autots.models.basics.FFT"]], "glm (class in autots.models.statsmodels)": [[4, "autots.models.statsmodels.GLM"]], "gls (class in autots.models.statsmodels)": [[4, "autots.models.statsmodels.GLS"]], "gluonts (class in autots.models.gluonts)": [[4, "autots.models.gluonts.GluonTS"]], "greykite (class in autots.models.greykite)": [[4, "autots.models.greykite.Greykite"]], "hdistensemble() (in module autots.models.ensemble)": [[4, "autots.models.ensemble.HDistEnsemble"]], "horizontalensemble() (in module autots.models.ensemble)": [[4, "autots.models.ensemble.HorizontalEnsemble"]], "horizontaltemplategenerator() (in module autots.models.ensemble)": [[4, "autots.models.ensemble.HorizontalTemplateGenerator"]], "kalmanstatespace (class in autots.models.basics)": [[4, "autots.models.basics.KalmanStateSpace"]], "kerasrnn (class in autots.models.dnn)": [[4, "autots.models.dnn.KerasRNN"]], "latc (class in autots.models.matrix_var)": [[4, "autots.models.matrix_var.LATC"]], "lastvaluenaive (class in autots.models.basics)": [[4, 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in autots.models.prophet)": [[4, "autots.models.prophet.NeuralProphet"]], "predictionobject (class in autots.models.base)": [[4, "autots.models.base.PredictionObject"]], "preprocessingregression (class in autots.models.sklearn)": [[4, "autots.models.sklearn.PreprocessingRegression"]], "pytorchforecasting (class in autots.models.pytorch)": [[4, "autots.models.pytorch.PytorchForecasting"]], "rrvar (class in autots.models.matrix_var)": [[4, "autots.models.matrix_var.RRVAR"]], "rollingregression (class in autots.models.sklearn)": [[4, "autots.models.sklearn.RollingRegression"]], "seasonalnaive (class in autots.models.basics)": [[4, "autots.models.basics.SeasonalNaive"]], "seasonalitymotif (class in autots.models.basics)": [[4, "autots.models.basics.SeasonalityMotif"]], "sectionalmotif (class in autots.models.basics)": [[4, "autots.models.basics.SectionalMotif"]], "tfpregression (class in autots.models.tfp)": [[4, "autots.models.tfp.TFPRegression"]], "tfpregressor (class in autots.models.tfp)": [[4, "autots.models.tfp.TFPRegressor"]], "tmf (class in autots.models.matrix_var)": [[4, "autots.models.matrix_var.TMF"]], "tensorflowsts (class in autots.models.tfp)": [[4, "autots.models.tfp.TensorflowSTS"]], "theta (class in autots.models.statsmodels)": [[4, "autots.models.statsmodels.Theta"]], "tide (class in autots.models.tide)": [[4, "autots.models.tide.TiDE"]], "timecovariates (class in autots.models.tide)": [[4, "autots.models.tide.TimeCovariates"]], "timeseriesdata (class in autots.models.tide)": [[4, "autots.models.tide.TimeSeriesdata"]], "transformer (class in autots.models.dnn)": [[4, "autots.models.dnn.Transformer"]], "univariateregression (class in autots.models.sklearn)": [[4, "autots.models.sklearn.UnivariateRegression"]], "unobservedcomponents (class in autots.models.statsmodels)": [[4, "autots.models.statsmodels.UnobservedComponents"]], "var (class in autots.models.statsmodels)": [[4, "autots.models.statsmodels.VAR"]], "varmax (class in autots.models.statsmodels)": [[4, "autots.models.statsmodels.VARMAX"]], "vecm (class in autots.models.statsmodels)": [[4, "autots.models.statsmodels.VECM"]], "vectorizedmultioutputgpr (class in autots.models.sklearn)": [[4, "autots.models.sklearn.VectorizedMultiOutputGPR"]], "windowregression (class in autots.models.sklearn)": [[4, "autots.models.sklearn.WindowRegression"]], "zeroesnaive (in module autots.models.basics)": [[4, "autots.models.basics.ZeroesNaive"]], "analyze_trend() (autots.models.cassandra.cassandra method)": [[4, "autots.models.cassandra.Cassandra.analyze_trend"]], "anomalies (autots.models.cassandra.cassandra..anomaly_detector attribute)": [[4, "autots.models.cassandra.Cassandra..anomaly_detector.anomalies"]], "apply_constraints() (autots.models.base.predictionobject method)": [[4, "autots.models.base.PredictionObject.apply_constraints"], [4, "id0"]], "apply_constraints() (in module autots.models.base)": [[4, "autots.models.base.apply_constraints"]], "arima_seek_the_oracle() (in module autots.models.statsmodels)": [[4, "autots.models.statsmodels.arima_seek_the_oracle"]], "auto_fit() (autots.models.cassandra.cassandra method)": [[4, "autots.models.cassandra.Cassandra.auto_fit"]], "auto_model_list() (in module autots.models.model_list)": [[4, "autots.models.model_list.auto_model_list"]], "autots.models": [[4, "module-autots.models"]], "autots.models.arch": [[4, "module-autots.models.arch"]], "autots.models.base": [[4, "module-autots.models.base"]], "autots.models.basics": [[4, "module-autots.models.basics"]], "autots.models.cassandra": [[4, "module-autots.models.cassandra"]], "autots.models.dnn": [[4, "module-autots.models.dnn"]], "autots.models.ensemble": [[4, "module-autots.models.ensemble"]], "autots.models.gluonts": [[4, "module-autots.models.gluonts"]], "autots.models.greykite": [[4, "module-autots.models.greykite"]], "autots.models.matrix_var": [[4, "module-autots.models.matrix_var"]], "autots.models.mlensemble": [[4, 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module autots.models.cassandra)": [[4, "autots.models.cassandra.cost_function_l2"]], "cost_function_quantile() (in module autots.models.cassandra)": [[4, "autots.models.cassandra.cost_function_quantile"]], "create_feature() (in module autots.models.mlensemble)": [[4, "autots.models.mlensemble.create_feature"]], "create_forecast_index() (autots.models.base.modelobject method)": [[4, "autots.models.base.ModelObject.create_forecast_index"]], "create_forecast_index() (autots.models.cassandra.cassandra method)": [[4, "autots.models.cassandra.Cassandra.create_forecast_index"]], "create_forecast_index() (in module autots.models.base)": [[4, "autots.models.base.create_forecast_index"]], "create_seaborn_palette_from_cmap() (in module autots.models.base)": [[4, "autots.models.base.create_seaborn_palette_from_cmap"]], "create_t() (autots.models.cassandra.cassandra method)": [[4, "autots.models.cassandra.Cassandra.create_t"]], "create_t() (in module autots.models.cassandra)": [[4, 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"autots.tools.window_functions.window_lin_reg"]], "window_lin_reg_mean() (in module autots.tools.window_functions)": [[6, "autots.tools.window_functions.window_lin_reg_mean"]], "window_lin_reg_mean_no_nan() (in module autots.tools.window_functions)": [[6, "autots.tools.window_functions.window_lin_reg_mean_no_nan"]], "window_maker() (in module autots.tools.window_functions)": [[6, "autots.tools.window_functions.window_maker"]], "window_maker_2() (in module autots.tools.window_functions)": [[6, "autots.tools.window_functions.window_maker_2"]], "window_maker_3() (in module autots.tools.window_functions)": [[6, "autots.tools.window_functions.window_maker_3"]], "window_sum_mean() (in module autots.tools.window_functions)": [[6, "autots.tools.window_functions.window_sum_mean"]], "window_sum_mean_nan_tail() (in module autots.tools.window_functions)": [[6, "autots.tools.window_functions.window_sum_mean_nan_tail"]], "window_sum_nan_mean() (in module autots.tools.window_functions)": [[6, "autots.tools.window_functions.window_sum_nan_mean"]], "zscore_survival_function() (in module autots.tools.anomaly_utils)": [[6, "autots.tools.anomaly_utils.zscore_survival_function"]]}}) \ No newline at end of file diff --git a/docs/build/html/source/autots.evaluator.html b/docs/build/html/source/autots.evaluator.html index 4b4d5e5f..edebcccf 100644 --- a/docs/build/html/source/autots.evaluator.html +++ b/docs/build/html/source/autots.evaluator.html @@ -244,7 +244,7 @@

Submodules
-class autots.evaluator.auto_model.TemplateEvalObject(model_results=Empty DataFrame Columns: [] Index: [], per_timestamp_smape=Empty DataFrame Columns: [] Index: [], per_series_metrics=Empty DataFrame Columns: [] Index: [], per_series_mae=None, per_series_rmse=None, per_series_made=None, per_series_contour=None, per_series_spl=None, per_series_mle=None, per_series_imle=None, per_series_maxe=None, per_series_oda=None, per_series_mqae=None, per_series_dwae=None, per_series_ewmae=None, per_series_uwmse=None, per_series_smoothness=None, per_series_mate=None, per_series_wasserstein=None, per_series_dwd=None, model_count: int = 0)¶
+class autots.evaluator.auto_model.TemplateEvalObject(model_results=Empty DataFrame Columns: [] Index: [], per_timestamp_smape=Empty DataFrame Columns: [] Index: [], per_series_metrics=Empty DataFrame Columns: [] Index: [], per_series_mae=None, per_series_rmse=None, per_series_made=None, per_series_contour=None, per_series_spl=None, per_series_mle=None, per_series_imle=None, per_series_maxe=None, per_series_oda=None, per_series_mqae=None, per_series_dwae=None, per_series_ewmae=None, per_series_uwmse=None, per_series_smoothness=None, per_series_mate=None, per_series_matse=None, per_series_wasserstein=None, per_series_dwd=None, model_count: int = 0)¶

Bases: object

Object to contain all the failures!.

diff --git a/docs/build/html/source/autots.models.html b/docs/build/html/source/autots.models.html index 8d69bd81..be20b2c7 100644 --- a/docs/build/html/source/autots.models.html +++ b/docs/build/html/source/autots.models.html @@ -2612,7 +2612,7 @@

Submodules
-fit(df, future_regressor=None)¶
+fit(df, future_regressor=None, static_regressor=None)¶

Train algorithm given data supplied.

Parameters:
@@ -2921,7 +2921,7 @@

Submodules
-fit(df, future_regressor=None)¶
+fit(df, future_regressor=None, static_regressor=None, regressor_per_series=None)¶

Train algorithm given data supplied.

Parameters:
@@ -2949,7 +2949,7 @@

Submodules
-predict(forecast_length: int | None = None, future_regressor=None, just_point_forecast: bool = False, df=None)¶
+predict(forecast_length: int | None = None, future_regressor=None, just_point_forecast: bool = False, df=None, regressor_per_series=None)¶

Generate forecast data immediately following dates of index supplied to .fit().

Parameters:
@@ -2970,7 +2970,7 @@

Submodules
-class autots.models.sklearn.MultivariateRegression(name: str = 'MultivariateRegression', frequency: str = 'infer', prediction_interval: float = 0.9, regression_type: str | None = None, holiday_country: str = 'US', verbose: int = 0, random_seed: int = 2020, forecast_length: int = 7, regression_model: dict = {'model': 'RandomForest', 'model_params': {}}, holiday: bool = False, mean_rolling_periods: int = 30, macd_periods: int | None = None, std_rolling_periods: int = 7, max_rolling_periods: int = 7, min_rolling_periods: int = 7, ewm_var_alpha: float | None = None, quantile90_rolling_periods: int | None = None, quantile10_rolling_periods: int | None = None, ewm_alpha: float = 0.5, additional_lag_periods: int | None = None, abs_energy: bool = False, rolling_autocorr_periods: int | None = None, nonzero_last_n: int | None = None, datepart_method: str | None = None, polynomial_degree: int | None = None, window: int = 5, probabilistic: bool = False, scale_full_X: bool = False, quantile_params: dict = {'learning_rate': 0.1, 'max_depth': 20, 'min_samples_leaf': 4, 'min_samples_split': 5, 'n_estimators': 250}, cointegration: str | None = None, cointegration_lag: int = 1, n_jobs: int = -1, **kwargs)¶
+class autots.models.sklearn.MultivariateRegression(name: str = 'MultivariateRegression', frequency: str = 'infer', prediction_interval: float = 0.9, regression_type: str | None = None, holiday_country: str = 'US', verbose: int = 0, random_seed: int = 2020, forecast_length: int = 7, regression_model: dict = {'model': 'RandomForest', 'model_params': {}}, holiday: bool = False, mean_rolling_periods: int = 30, macd_periods: int | None = None, std_rolling_periods: int = 7, max_rolling_periods: int = 7, min_rolling_periods: int = 7, ewm_var_alpha: float | None = None, quantile90_rolling_periods: int | None = None, quantile10_rolling_periods: int | None = None, ewm_alpha: float = 0.5, additional_lag_periods: int | None = None, abs_energy: bool = False, rolling_autocorr_periods: int | None = None, nonzero_last_n: int | None = None, datepart_method: str | None = None, polynomial_degree: int | None = None, window: int = 5, probabilistic: bool = False, scale_full_X: bool = False, quantile_params: dict = {'learning_rate': 0.1, 'max_depth': 20, 'min_samples_leaf': 4, 'min_samples_split': 5, 'n_estimators': 250}, cointegration: str | None = None, cointegration_lag: int = 1, series_hash: bool = False, n_jobs: int = -1, **kwargs)¶

Bases: ModelObject

Regression-framed approach to forecasting using sklearn. A multiariate version of rolling regression: ie each series is lagged independently but modeled together

@@ -2992,7 +2992,7 @@

Submodules
-fit(df, future_regressor=None)¶
+fit(df, future_regressor=None, static_regressor=None, regressor_per_series=None)¶

Train algorithm given data supplied.

Parameters:
@@ -3006,7 +3006,7 @@

Submodules
-fit_data(df, future_regressor=None)¶
+fit_data(df, future_regressor=None, static_regressor=None, regressor_per_series=None)¶

@@ -3023,7 +3023,7 @@

Submodules
-predict(forecast_length: int | None = None, just_point_forecast: bool = False, future_regressor=None, df=None)¶
+predict(forecast_length: int | None = None, just_point_forecast: bool = False, future_regressor=None, df=None, regressor_per_series=None)¶

Generate forecast data immediately following dates of index supplied to .fit().

Parameters:
@@ -3301,7 +3301,7 @@

Submodules
-fit(df, future_regressor=None)¶
+fit(df, future_regressor=None, static_regressor=None)¶

Train algorithm given data supplied.

Parameters:
@@ -3383,7 +3383,7 @@

Submodules
-autots.models.sklearn.rolling_x_regressor_regressor(df, mean_rolling_periods: int = 30, macd_periods: int | None = None, std_rolling_periods: int = 7, max_rolling_periods: int | None = None, min_rolling_periods: int | None = None, quantile90_rolling_periods: int | None = None, quantile10_rolling_periods: int | None = None, ewm_alpha: float = 0.5, ewm_var_alpha: float | None = None, additional_lag_periods: int = 7, abs_energy: bool = False, rolling_autocorr_periods: int | None = None, nonzero_last_n: int | None = None, add_date_part: str | None = None, holiday: bool = False, holiday_country: str = 'US', polynomial_degree: int | None = None, window: int | None = None, future_regressor=None, cointegration: str | None = None, cointegration_lag: int = 1)¶
+autots.models.sklearn.rolling_x_regressor_regressor(df, mean_rolling_periods: int = 30, macd_periods: int | None = None, std_rolling_periods: int = 7, max_rolling_periods: int | None = None, min_rolling_periods: int | None = None, quantile90_rolling_periods: int | None = None, quantile10_rolling_periods: int | None = None, ewm_alpha: float = 0.5, ewm_var_alpha: float | None = None, additional_lag_periods: int = 7, abs_energy: bool = False, rolling_autocorr_periods: int | None = None, nonzero_last_n: int | None = None, add_date_part: str | None = None, holiday: bool = False, holiday_country: str = 'US', polynomial_degree: int | None = None, window: int | None = None, future_regressor=None, regressor_per_series=None, static_regressor=None, cointegration: str | None = None, cointegration_lag: int = 1, series_id=None)¶

Adds in the future_regressor.

diff --git a/docs/conf.py b/docs/conf.py index 4dcdee24..8fa0ba22 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -25,7 +25,7 @@ # import AutoTS # from AutoTS import __version__ # release = __version__ -release = "0.6.8" +release = "0.6.9" # -- General configuration --------------------------------------------------- diff --git a/production_example.py b/production_example.py index 301b67cb..96abb107 100644 --- a/production_example.py +++ b/production_example.py @@ -54,7 +54,7 @@ save_location = None # "C:/Users/Colin/Downloads" # directory to save templates to. Defaults to working dir template_filename = f"autots_forecast_template_{forecast_name}.csv" forecast_csv_name = None # f"autots_forecast_{forecast_name}.csv" # or None, point forecast only is written -model_list = "scalable" +model_list = 'fast_parallel' transformer_list = "fast" # 'superfast' transformer_max_depth = 5 models_mode = "default" # "deep", "regressor" @@ -161,7 +161,7 @@ if trend_list is not None: for tx in trend_list: if tx in df.columns: - df[tx] = df[tx].interpolate('akima').fillna(method='ffill', limit=30).fillna(method='bfill', limit=30) + df[tx] = df[tx].interpolate('akima').ffill(limit=30).bfill(limit=30) # fill weekends if tickers is not None: for fx in tickers: @@ -178,7 +178,7 @@ df[wevnt] = df[wevnt].mask(df[wevnt].notnull().cummax(), df[wevnt].fillna(0)) # most of the NaN here are just weekends, when financial series aren't collected, ffill of a few steps is fine # partial forward fill, no back fill - df = df.fillna(method='ffill', limit=3) + df = df.ffill(limit=3) df = df[df.index.year > 1999] # remove any data from the future @@ -226,18 +226,19 @@ """ metric_weighting = { - 'smape_weighting': 1, - 'mae_weighting': 3, - 'rmse_weighting': 2, + 'smape_weighting': 2, + 'mae_weighting': 2, + 'rmse_weighting': 1.5, 'made_weighting': 1, 'mage_weighting': 0, 'mate_weighting': 0.01, - 'mle_weighting': 1, + 'mle_weighting': 0.1, 'imle_weighting': 0, - 'spl_weighting': 5, + 'spl_weighting': 3, 'dwae_weighting': 1, 'uwmse_weighting': 1, 'dwd_weighting': 0.1, + "oda_weighting": 0.1, 'runtime_weighting': 0.05, } diff --git a/pyproject.toml b/pyproject.toml index 25207914..2ff1b288 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta" [project] name = "autots" -version = "0.6.8" +version = "0.6.9" authors = [ { name="Colin Catlin", email="colin.catlin@gmail.com" }, ] diff --git a/setup.py b/setup.py index b0b4c394..39403503 100644 --- a/setup.py +++ b/setup.py @@ -32,7 +32,7 @@ setuptools.setup( name="AutoTS", - version="0.6.8", + version="0.6.9", author="Colin Catlin", author_email="colin.catlin@syllepsis.live", description="Automated Time Series Forecasting",