autots.models package¶
Submodules¶
autots.models.basics module¶
Naives and Others Requiring No Additional Packages Beyond Numpy and Pandas
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class
autots.models.basics.
AverageValueNaive
(name: str = 'AverageValueNaive', frequency: str = 'infer', prediction_interval: float = 0.9, holiday_country: str = 'US', random_seed: int = 2020, verbose: int = 0, method: str = 'Median', **kwargs)¶ Bases:
autots.evaluator.auto_model.ModelObject
Naive forecasting predicting a dataframe of the series’ median values
- Parameters
name (str) – String to identify class
frequency (str) – String alias of datetime index frequency or else ‘infer’
prediction_interval (float) – Confidence interval for probabilistic forecast
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fit
(df, future_regressor=[])¶ Train algorithm given data supplied.
- Parameters
df (pandas.DataFrame) – Datetime Indexed
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get_new_params
(method: str = 'random')¶ Returns dict of new parameters for parameter tuning
-
get_params
()¶ Return dict of current parameters.
-
predict
(forecast_length: int, future_regressor=[], just_point_forecast=False)¶ Generates forecast data immediately following dates of index supplied to .fit()
- Parameters
forecast_length (int) – Number of periods of data to forecast ahead
regressor (numpy.Array) – additional regressor, not used
just_point_forecast (bool) – If True, return a pandas.DataFrame of just point forecasts
- Returns
Either a PredictionObject of forecasts and metadata, or if just_point_forecast == True, a dataframe of point forecasts
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class
autots.models.basics.
LastValueNaive
(name: str = 'LastValueNaive', frequency: str = 'infer', prediction_interval: float = 0.9, holiday_country: str = 'US', random_seed: int = 2020, **kwargs)¶ Bases:
autots.evaluator.auto_model.ModelObject
Naive forecasting predicting a dataframe of the last series value
- Parameters
name (str) – String to identify class
frequency (str) – String alias of datetime index frequency or else ‘infer’
prediction_interval (float) – Confidence interval for probabilistic forecast
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fit
(df, future_regressor=[])¶ Train algorithm given data supplied
- Parameters
df (pandas.DataFrame) – Datetime Indexed
-
get_new_params
(method: str = 'random')¶ Returns dict of new parameters for parameter tuning
-
get_params
()¶ Return dict of current parameters
-
predict
(forecast_length: int, future_regressor=[], just_point_forecast=False)¶ Generates forecast data immediately following dates of index supplied to .fit()
- Parameters
forecast_length (int) – Number of periods of data to forecast ahead
regressor (numpy.Array) – additional regressor, not used
just_point_forecast (bool) – If True, return a pandas.DataFrame of just point forecasts
- Returns
Either a PredictionObject of forecasts and metadata, or if just_point_forecast == True, a dataframe of point forecasts
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class
autots.models.basics.
MotifSimulation
(name: str = 'MotifSimulation', frequency: str = 'infer', prediction_interval: float = 0.9, holiday_country: str = 'US', random_seed: int = 2020, phrase_len: str = '5', comparison: str = 'magnitude_pct_change_sign', shared: bool = False, distance_metric: str = 'l2', max_motifs: float = 50, recency_weighting: float = 0.1, cutoff_threshold: float = 0.9, cutoff_minimum: int = 20, point_method: str = 'median', verbose: int = 1, **kwargs)¶ Bases:
autots.evaluator.auto_model.ModelObject
More dark magic created by the evil mastermind of this project. Basically a highly-customized KNN
Warning: if you are forecasting many steps (large forecast_length), and interested in probabilistic upper/lower forecasts, then set recency_weighting <= 0, and have a larger cutoff_minimum
- Parameters
name (str) – String to identify class
frequency (str) – String alias of datetime index frequency or else ‘infer’
prediction_interval (float) – Confidence interval for probabilistic forecast
phrase_len (int) – length of motif vectors to compare as samples
comparison (str) – method to process data before comparison, ‘magnitude’ is original data
shared (bool) – whether to compare motifs across all series together, or separately
distance_metric (str) – passed through to sklearn pairwise_distances
max_motifs (float) – number of motifs to compare per series. If less 1, used as % of length training data
recency_weighting (float) – amount to the value of more recent data.
cutoff_threshold (float) – lowest value of distance metric to allow into forecast
cutoff_minimum (int) – minimum number of motif vectors to include in forecast.
point_method (str) – summarization method to choose forecast on, ‘sample’, ‘mean’, ‘sign_biased_mean’, ‘median’
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fit
(df, future_regressor=[])¶ Train algorithm given data supplied.
- Parameters
df (pandas.DataFrame) – Datetime Indexed
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get_new_params
(method: str = 'random')¶ Return dict of new parameters for parameter tuning.
-
get_params
()¶ Return dict of current parameters.
-
predict
(forecast_length: int, future_regressor=[], just_point_forecast=False)¶ Generates forecast data immediately following dates of index supplied to .fit()
- Parameters
forecast_length (int) – Number of periods of data to forecast ahead
regressor (numpy.Array) – additional regressor, not used
just_point_forecast (bool) – If True, return a pandas.DataFrame of just point forecasts
- Returns
Either a PredictionObject of forecasts and metadata, or if just_point_forecast == True, a dataframe of point forecasts
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class
autots.models.basics.
SeasonalNaive
(name: str = 'SeasonalNaive', frequency: str = 'infer', prediction_interval: float = 0.9, holiday_country: str = 'US', random_seed: int = 2020, verbose: int = 0, lag_1: int = 7, lag_2: int = None, method: str = 'LastValue', **kwargs)¶ Bases:
autots.evaluator.auto_model.ModelObject
Naive forecasting predicting a dataframe with seasonal (lag) forecasts.
Concerto No. 2 in G minor, Op. 8, RV 315
- Parameters
name (str) – String to identify class
frequency (str) – String alias of datetime index frequency or else ‘infer’
prediction_interval (float) – Confidence interval for probabilistic forecast
method (str) – Either ‘LastValue’ (use last value of lag n) or ‘Mean’ (avg of all lag n)
lag_1 (int) – The lag of the seasonality, should int > 1.
lag_2 (int) – Optional second lag of seasonality which is averaged with first lag to produce forecast.
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fit
(df, future_regressor=[])¶ Train algorithm given data supplied.
- Parameters
df (pandas.DataFrame) – Datetime Indexed
-
get_new_params
(method: str = 'random')¶ Return dict of new parameters for parameter tuning.
-
get_params
()¶ Return dict of current parameters.
-
predict
(forecast_length: int, future_regressor=[], just_point_forecast: bool = False)¶ Generate forecast data immediately following dates of .fit().
- Parameters
forecast_length (int) – Number of periods of data to forecast ahead
regressor (numpy.Array) – additional regressor, not used
just_point_forecast (bool) – If True, return a pandas.DataFrame of just point forecasts
- Returns
Either a PredictionObject of forecasts and metadata, or if just_point_forecast == True, a dataframe of point forecasts
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class
autots.models.basics.
ZeroesNaive
(name: str = 'ZeroesNaive', frequency: str = 'infer', prediction_interval: float = 0.9, holiday_country: str = 'US', random_seed: int = 2020, verbose: int = 0, **kwargs)¶ Bases:
autots.evaluator.auto_model.ModelObject
Naive forecasting predicting a dataframe of zeroes (0’s)
- Parameters
name (str) – String to identify class
frequency (str) – String alias of datetime index frequency or else ‘infer’
prediction_interval (float) – Confidence interval for probabilistic forecast
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fit
(df, future_regressor=[])¶ Train algorithm given data supplied
- Parameters
df (pandas.DataFrame) – Datetime Indexed
-
get_new_params
(method: str = 'random')¶ Returns dict of new parameters for parameter tuning
-
get_params
()¶ Return dict of current parameters
-
predict
(forecast_length: int, future_regressor=[], just_point_forecast=False)¶ Generates forecast data immediately following dates of index supplied to .fit()
- Parameters
forecast_length (int) – Number of periods of data to forecast ahead
regressor (numpy.Array) – additional regressor, not used
just_point_forecast (bool) – If True, return a pandas.DataFrame of just point forecasts
- Returns
Either a PredictionObject of forecasts and metadata, or if just_point_forecast == True, a dataframe of point forecasts
autots.models.dnn module¶
Neural Nets.
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class
autots.models.dnn.
KerasRNN
(rnn_type: str = 'LSTM', kernel_initializer: str = 'lecun_uniform', hidden_layer_sizes: tuple = (32, 32, 32), optimizer: str = 'adam', loss: str = 'huber', epochs: int = 50, batch_size: int = 32, verbose: int = 1, random_seed: int = 2020)¶ Bases:
object
Wrapper for Tensorflow Keras based RNN.
- Parameters
rnn_type (str) – Keras cell type ‘GRU’ or default ‘LSTM’
kernel_initializer (str) – passed to first keras LSTM or GRU layer
hidden_layer_sizes (tuple) – of len 1 or 3 passed to first keras LSTM or GRU layers
optimizer (str) – Passed to keras model.compile
loss (str) – Passed to keras model.compile
epochs (int) – Passed to keras model.fit
batch_size (int) – Passed to keras model.fit
verbose (int) – 0, 1 or 2. Passed to keras model.fit
random_seed (int) – passed to tf.random.set_seed()
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fit
(X, Y)¶ Train the model on dataframes of X and Y.
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predict
(X)¶ Predict on dataframe of X.
autots.models.ensemble module¶
Tools for generating and forecasting with ensembles of models.
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autots.models.ensemble.
Best3Ensemble
(ensemble_params, forecasts_list, forecasts, lower_forecasts, upper_forecasts, forecasts_runtime, prediction_interval)¶ Generate mean forecast for ensemble of models.
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autots.models.ensemble.
DistEnsemble
(ensemble_params, forecasts_list, forecasts, lower_forecasts, upper_forecasts, forecasts_runtime, prediction_interval)¶ Generate forecast for distance ensemble.
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autots.models.ensemble.
EnsembleForecast
(ensemble_str, ensemble_params, forecasts_list, forecasts, lower_forecasts, upper_forecasts, forecasts_runtime, prediction_interval)¶ Return PredictionObject for given ensemble method.
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autots.models.ensemble.
EnsembleTemplateGenerator
(initial_results, forecast_length: int = 14, ensemble: str = 'simple')¶ Generate ensemble templates given a table of results.
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autots.models.ensemble.
HDistEnsemble
(ensemble_params, forecasts_list, forecasts, lower_forecasts, upper_forecasts, forecasts_runtime, prediction_interval)¶ Generate forecast for per_series per distance ensembling.
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autots.models.ensemble.
HorizontalEnsemble
(ensemble_params, forecasts_list, forecasts, lower_forecasts, upper_forecasts, forecasts_runtime, prediction_interval)¶ Generate forecast for per_series ensembling.
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autots.models.ensemble.
HorizontalTemplateGenerator
(per_series, model_results, forecast_length: int = 14, ensemble: str = 'horizontal', subset_flag: bool = True, per_series2=None)¶ Generate horizontal ensemble templates given a table of results.
autots.models.gluonts module¶
GluonTS
Excellent models, released by Amazon, scale well. Except it is really the only thing I use that runs mxnet, and it takes a while to train these guys…
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class
autots.models.gluonts.
GluonTS
(name: str = 'GluonTS', frequency: str = 'infer', prediction_interval: float = 0.9, regression_type: str = None, holiday_country: str = 'US', random_seed: int = 2020, verbose: int = 0, gluon_model: str = 'DeepAR', epochs: int = 20, learning_rate: float = 0.001, context_length=10, forecast_length: int = 14)¶ Bases:
autots.evaluator.auto_model.ModelObject
GluonTS based on mxnet.
- Parameters
name (str) – String to identify class
frequency (str) – String alias of datetime index frequency or else ‘infer’
prediction_interval (float) – Confidence interval for probabilistic forecast
regression_type (str) – Not yet implemented
gluon_model (str) – Model Structure to Use - [‘DeepAR’, ‘NPTS’, ‘DeepState’, ‘WaveNet’,’DeepFactor’, ‘Transformer’,’SFF’, ‘MQCNN’]
epochs (int) – Number of neural network training epochs. Higher generally results in better, then over fit.
learning_rate (float) – Neural net training parameter
context_length (str) – int window, ‘2ForecastLength’, or ‘nForecastLength’
forecast_length (int) – Length to forecast. Unlike in other methods, this must be provided before fitting model
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fit
(df, future_regressor=[])¶ Train algorithm given data supplied.
- Parameters
df (pandas.DataFrame) – Datetime Indexed
-
get_new_params
(method: str = 'random')¶ Return dict of new parameters for parameter tuning.
-
get_params
()¶ Return dict of current parameters.
-
predict
(forecast_length: int, future_regressor=[], just_point_forecast=False)¶ Generates forecast data immediately following dates of index supplied to .fit()
- Parameters
forecast_length (int) – Number of periods of data to forecast ahead
regressor (numpy.Array) – additional regressor, not used
just_point_forecast (bool) – If True, return a pandas.DataFrame of just point forecasts
- Returns
Either a PredictionObject of forecasts and metadata, or if just_point_forecast == True, a dataframe of point forecasts
autots.models.prophet module¶
Facebook’s Prophet
Since Prophet install can be finicky on Windows, it will be an optional dependency.
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class
autots.models.prophet.
FBProphet
(name: str = 'FBProphet', frequency: str = 'infer', prediction_interval: float = 0.9, holiday: bool = False, regression_type: str = None, holiday_country: str = 'US', random_seed: int = 2020, verbose: int = 0, n_jobs: int = None)¶ Bases:
autots.evaluator.auto_model.ModelObject
Facebook’s Prophet
‘thou shall count to 3, no more, no less, 3 shall be the number thou shall count, and the number of the counting shall be 3. 4 thou shall not count, neither count thou 2, excepting that thou then preceed to 3.’ -Python
- Parameters
name (str) – String to identify class
frequency (str) – String alias of datetime index frequency or else ‘infer’
prediction_interval (float) – Confidence interval for probabilistic forecast
holiday (bool) – If true, include holidays
regression_type (str) – type of regression (None, ‘User’)
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fit
(df, future_regressor=[])¶ Train algorithm given data supplied.
- Parameters
df (pandas.DataFrame) – Datetime Indexed
-
get_new_params
(method: str = 'random')¶ Return dict of new parameters for parameter tuning.
-
get_params
()¶ Return dict of current parameters.
-
predict
(forecast_length: int, future_regressor=[], just_point_forecast: bool = False)¶ Generates forecast data immediately following dates of index supplied to .fit()
- Parameters
forecast_length (int) – Number of periods of data to forecast ahead
regressor (numpy.Array) – additional regressor, not used
just_point_forecast (bool) – If True, return a pandas.DataFrame of just point forecasts
- Returns
Either a PredictionObject of forecasts and metadata, or if just_point_forecast == True, a dataframe of point forecasts
autots.models.sklearn module¶
Sklearn dependent models
Decision Tree, Elastic Net, Random Forest, MLPRegressor, KNN, Adaboost
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class
autots.models.sklearn.
ComponentAnalysis
(name: str = 'ComponentAnalysis', frequency: str = 'infer', prediction_interval: float = 0.9, holiday_country: str = 'US', random_seed: int = 2020, verbose: int = 0, n_components: int = 10, forecast_length: int = 14, model: str = 'GLS', model_parameters: dict = {}, decomposition: str = 'PCA', n_jobs: int = -1)¶ Bases:
autots.evaluator.auto_model.ModelObject
Forecasting on principle components.
- Parameters
name (str) – String to identify class
frequency (str) – String alias of datetime index frequency or else ‘infer’
prediction_interval (float) – Confidence interval for probabilistic forecast
model (str) – An AutoTS model str
model_parameters (dict) – parameters to pass to AutoTS model
n_components (int) – int or ‘NthN’ number of components to use
decomposition (str) – decomposition method to use from scikit-learn
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fit
(df, future_regressor=[])¶ Train algorithm given data supplied.
- Parameters
df (pandas.DataFrame) – Datetime Indexed
-
get_new_params
(method: str = 'random')¶ Return dict of new parameters for parameter tuning.
-
get_params
()¶ Return dict of current parameters.
-
predict
(forecast_length: int, future_regressor=[], just_point_forecast: bool = False)¶ Generate forecast data immediately following dates of .fit().
- Parameters
forecast_length (int) – Number of periods of data to forecast ahead
regressor (numpy.Array) – additional regressor, not used
just_point_forecast (bool) – If True, return a pandas.DataFrame of just point forecasts
- Returns
Either a PredictionObject of forecasts and metadata, or if just_point_forecast == True, a dataframe of point forecasts
-
class
autots.models.sklearn.
RollingRegression
(name: str = 'RollingRegression', frequency: str = 'infer', prediction_interval: float = 0.9, regression_type: str = None, holiday_country: str = 'US', verbose: int = 0, random_seed: int = 2020, regression_model: dict = {'model': 'Adaboost', 'model_params': {'base_estimator': 'DecisionTree', 'learning_rate': 1.0, 'loss': 'linear', 'n_estimators': 50}}, holiday: bool = False, mean_rolling_periods: int = 30, macd_periods: int = None, std_rolling_periods: int = 7, max_rolling_periods: int = 7, min_rolling_periods: int = 7, ewm_alpha: float = 0.5, additional_lag_periods: int = 7, abs_energy: bool = False, rolling_autocorr_periods: int = None, add_date_part: str = None, polynomial_degree: int = None, x_transform: str = None, n_jobs: int = -1, **kwargs)¶ Bases:
autots.evaluator.auto_model.ModelObject
General regression-framed approach to forecasting using sklearn.
Who are you who are so wise in the ways of science? I am Arthur, King of the Britons. -Python
- Parameters
name (str) – String to identify class
frequency (str) – String alias of datetime index frequency or else ‘infer’
prediction_interval (float) – Confidence interval for probabilistic forecast
holiday (bool) – If true, include holiday flags
regression_type (str) – type of regression (None, ‘User’)
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fit
(df, future_regressor=[])¶ Train algorithm given data supplied.
- Parameters
df (pandas.DataFrame) – Datetime Indexed
future_regressor (pandas.DataFrame or Series) – Datetime Indexed
-
get_new_params
(method: str = 'random')¶ Return dict of new parameters for parameter tuning.
-
get_params
()¶ Return dict of current parameters.
-
predict
(forecast_length: int, future_regressor=[], just_point_forecast: bool = False)¶ Generate forecast data immediately following dates of index supplied to .fit().
- Parameters
forecast_length (int) – Number of periods of data to forecast ahead
regressor (numpy.Array) – additional regressor
just_point_forecast (bool) – If True, return a pandas.DataFrame of just point forecasts
- Returns
Either a PredictionObject of forecasts and metadata, or if just_point_forecast == True, a dataframe of point forecasts
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class
autots.models.sklearn.
WindowRegression
(name: str = 'WindowRegression', frequency: str = 'infer', prediction_interval: float = 0.9, holiday_country: str = 'US', random_seed: int = 2020, verbose: int = 0, window_size: int = 10, regression_model: dict = {'model': 'Adaboost', 'model_params': {'base_estimator': 'DecisionTree', 'learning_rate': 1.0, 'loss': 'linear', 'n_estimators': 50}}, input_dim: str = 'multivariate', output_dim: str = '1step', normalize_window: bool = False, shuffle: bool = True, forecast_length: int = 1, max_windows: int = 5000, n_jobs: int = -1, **kwargs)¶ Bases:
autots.evaluator.auto_model.ModelObject
Regression use the last n values as the basis of training data.
- Parameters
name (str) – String to identify class
frequency (str) – String alias of datetime index frequency or else ‘infer’
prediction_interval (float) – Confidence interval for probabilistic forecast
transfer_learning (#) – str = None,
transfer_learning_transformation (#) – dict = None,
regression_type (#) – str = None,
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fit
(df, future_regressor=[])¶ Train algorithm given data supplied.
- Parameters
df (pandas.DataFrame) – Datetime Indexed
-
get_new_params
(method: str = 'random')¶ Return dict of new parameters for parameter tuning.
-
get_params
()¶ Return dict of current parameters.
-
predict
(forecast_length: int, future_regressor=[], just_point_forecast: bool = False)¶ Generate forecast data immediately following dates of .fit().
- Parameters
forecast_length (int) – Number of periods of data to forecast ahead
regressor (numpy.Array) – additional regressor, not used
just_point_forecast (bool) – If True, return a pandas.DataFrame of just point forecasts
- Returns
Either a PredictionObject of forecasts and metadata, or if just_point_forecast == True, a dataframe of point forecasts
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autots.models.sklearn.
date_part
(DTindex, method: str = 'simple')¶ Create date part columns from pd.DatetimeIndex.
- Parameters
DTindex (pd.DatetimeIndex) – datetime index to provide dates
method (str) – expanded, recurring, or simple simple - just day, year, month, weekday expanded - all available futures recurring - all features that should commonly repeat without aging
- Returns
pd.Dataframe with DTindex
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autots.models.sklearn.
generate_regressor_params
(models: list = ['RandomForest', 'ElasticNet', 'MLP', 'DecisionTree', 'KNN', 'Adaboost', 'SVM', 'BayesianRidge', 'xgboost', 'KerasRNN', 'HistGradientBoost', 'LightGBM'], model_probs: list = [0.05, 0.05, 0.14, 0.25, 0.1, 0.14, 0.02, 0.08, 0.01, 0.05, 0.01, 0.1])¶ Generate new parameters for input to regressor.
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autots.models.sklearn.
last_window
(df, window_size: int = 10, input_dim: str = 'multivariate', normalize_window: bool = False)¶
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autots.models.sklearn.
retrieve_regressor
(regression_model: dict = {'model': 'Adaboost', 'model_params': {'base_estimator': 'DecisionTree', 'learning_rate': 1.0, 'loss': 'linear', 'n_estimators': 50}}, verbose: int = 0, verbose_bool: bool = False, random_seed: int = 2020, n_jobs: int = 1)¶ Convert a model param dict to model object for regression frameworks.
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autots.models.sklearn.
rolling_x_regressor
(df, mean_rolling_periods: int = 30, macd_periods: int = None, std_rolling_periods: int = 7, max_rolling_periods: int = None, min_rolling_periods: int = None, ewm_alpha: float = 0.5, additional_lag_periods: int = 7, abs_energy: bool = False, rolling_autocorr_periods: int = None, add_date_part: str = None, holiday: bool = False, holiday_country: str = 'US', polynomial_degree: int = None)¶ Generate more features from initial time series.
macd_periods ignored if mean_rolling is None.
Returns a dataframe of statistical features. Will need to be shifted by 1 or more to match Y for forecast.
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autots.models.sklearn.
window_maker
(df, window_size: int = 10, input_dim: str = 'multivariate', normalize_window: bool = False, shuffle: bool = True, output_dim: str = 'forecast_length', forecast_length: int = 1, max_windows: int = 5000)¶ Convert a dataset into slices with history and y forecast.
autots.models.statsmodels module¶
Statsmodels based forecasting models.
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class
autots.models.statsmodels.
ARIMA
(name: str = 'ARIMA', frequency: str = 'infer', prediction_interval: float = 0.9, p: int = 0, d: int = 1, q: int = 0, regression_type: str = None, holiday_country: str = 'US', random_seed: int = 2020, verbose: int = 0, n_jobs: int = None, **kwargs)¶ Bases:
autots.evaluator.auto_model.ModelObject
ARIMA from Statsmodels.
- Parameters
name (str) – String to identify class
frequency (str) – String alias of datetime index frequency or else ‘infer’
prediction_interval (float) – Confidence interval for probabilistic forecast
p (int) – is the number of autoregressive steps,
d (int) – is the number of differences needed for stationarity
q (int) – is the number of lagged forecast errors in the prediction.
regression_type (str) – type of regression (None, ‘User’, or ‘Holiday’)
n_jobs (int) – passed to joblib for multiprocessing. Set to none for context manager.
-
fit
(df, future_regressor=[])¶ Train algorithm given data supplied .
- Parameters
df (pandas.DataFrame) – Datetime Indexed
-
get_new_params
(method: str = 'random')¶ Return dict of new parameters for parameter tuning.
large p,d,q can be very slow (a p of 30 can take hours)
-
get_params
()¶ Return dict of current parameters.
-
predict
(forecast_length: int, future_regressor=[], just_point_forecast=False)¶ Generate forecast data immediately following dates of index supplied to .fit().
- Parameters
forecast_length (int) – Number of periods of data to forecast ahead
regressor (numpy.Array) – additional regressor, not used
just_point_forecast (bool) – If True, return a pandas.DataFrame of just point forecasts
- Returns
Either a PredictionObject of forecasts and metadata, or if just_point_forecast == True, a dataframe of point forecasts
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class
autots.models.statsmodels.
DynamicFactor
(name: str = 'DynamicFactor', frequency: str = 'infer', prediction_interval: float = 0.9, regression_type: str = None, holiday_country: str = 'US', random_seed: int = 2020, verbose: int = 0, k_factors: int = 1, factor_order: int = 0, **kwargs)¶ Bases:
autots.evaluator.auto_model.ModelObject
DynamicFactor from Statsmodels
- Parameters
name (str) – String to identify class
frequency (str) – String alias of datetime index frequency or else ‘infer’
prediction_interval (float) – Confidence interval for probabilistic forecast
regression_type (str) – type of regression (None, ‘User’, or ‘Holiday’)
-
fit
(df, future_regressor=[])¶ Train algorithm given data supplied.
- Parameters
df (pandas.DataFrame) – Datetime Indexed
-
get_new_params
(method: str = 'random')¶ Return dict of new parameters for parameter tuning.
-
get_params
()¶ Return dict of current parameters.
-
predict
(forecast_length: int, future_regressor=[], just_point_forecast=False)¶ Generates forecast data immediately following dates of index supplied to .fit()
- Parameters
forecast_length (int) – Number of periods of data to forecast ahead
regressor (numpy.Array) – additional regressor, not used
just_point_forecast (bool) – If True, return a pandas.DataFrame of just point forecasts
- Returns
- Either a PredictionObject of forecasts and metadata, or
if just_point_forecast == True, a dataframe of point forecasts
maModel = DynamicFactor(df_train, freq = ‘MS’, k_factors = 2, factor_order=2).fit() maPred = maModel.predict()
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class
autots.models.statsmodels.
ETS
(name: str = 'ETS', frequency: str = 'infer', prediction_interval: float = 0.9, damped: bool = False, trend: str = None, seasonal: str = None, seasonal_periods: int = None, holiday_country: str = 'US', random_seed: int = 2020, verbose: int = 0, n_jobs: int = None, **kwargs)¶ Bases:
autots.evaluator.auto_model.ModelObject
Exponential Smoothing from Statsmodels
- Parameters
name (str) – String to identify class
frequency (str) – String alias of datetime index frequency or else ‘infer’
prediction_interval (float) – Confidence interval for probabilistic forecast
damped (bool) – passed through to statsmodel ETS
trend (str) – passed through to statsmodel ETS
seasonal (bool) – passed through to statsmodel ETS
seasonal_periods (int) – passed through to statsmodel ETS
-
fit
(df, future_regressor=[])¶ Train algorithm given data supplied
- Parameters
df (pandas.DataFrame) – Datetime Indexed
-
get_new_params
(method: str = 'random')¶ Return dict of new parameters for parameter tuning.
-
get_params
()¶ Return dict of current parameters.
-
predict
(forecast_length: int, future_regressor=[], just_point_forecast=False)¶ Generates forecast data immediately following dates of index supplied to .fit()
- Parameters
forecast_length (int) – Number of periods of data to forecast ahead
regressor (numpy.Array) – additional regressor, not used
just_point_forecast (bool) – If True, return a pandas.DataFrame of just point forecasts
- Returns
Either a PredictionObject of forecasts and metadata, or if just_point_forecast == True, a dataframe of point forecasts
-
class
autots.models.statsmodels.
GLM
(name: str = 'GLM', frequency: str = 'infer', prediction_interval: float = 0.9, holiday_country: str = 'US', random_seed: int = 2020, regression_type: str = None, family='Gaussian', constant: bool = False, verbose: int = 1, n_jobs: int = None, **kwargs)¶ Bases:
autots.evaluator.auto_model.ModelObject
Simple linear regression from statsmodels
- Parameters
name (str) – String to identify class
frequency (str) – String alias of datetime index frequency or else ‘infer’
prediction_interval (float) – Confidence interval for probabilistic forecast
regression_type (str) – type of regression (None, ‘User’)
-
fit
(df, future_regressor=[])¶ Train algorithm given data supplied
- Parameters
df (pandas.DataFrame) – Datetime Indexed
-
get_new_params
(method: str = 'random')¶ Return dict of new parameters for parameter tuning.
-
get_params
()¶ Return dict of current parameters.
-
predict
(forecast_length: int, future_regressor=[], just_point_forecast=False)¶ Generates forecast data immediately following dates of index supplied to .fit()
- Parameters
forecast_length (int) – Number of periods of data to forecast ahead
regressor (numpy.Array) – additional regressor, not used
just_point_forecast (bool) – If True, return a pandas.DataFrame of just point forecasts
- Returns
Either a PredictionObject of forecasts and metadata, or if just_point_forecast == True, a dataframe of point forecasts
-
class
autots.models.statsmodels.
GLS
(name: str = 'GLS', frequency: str = 'infer', prediction_interval: float = 0.9, holiday_country: str = 'US', random_seed: int = 2020, **kwargs)¶ Bases:
autots.evaluator.auto_model.ModelObject
Simple linear regression from statsmodels
- Parameters
name (str) – String to identify class
frequency (str) – String alias of datetime index frequency or else ‘infer’
prediction_interval (float) – Confidence interval for probabilistic forecast
-
fit
(df, future_regressor=[])¶ Train algorithm given data supplied
- Parameters
df (pandas.DataFrame) – Datetime Indexed
-
get_new_params
(method: str = 'random')¶ Returns dict of new parameters for parameter tuning
-
get_params
()¶ Return dict of current parameters
-
predict
(forecast_length: int, future_regressor=[], just_point_forecast=False)¶ Generates forecast data immediately following dates of index supplied to .fit()
- Parameters
forecast_length (int) – Number of periods of data to forecast ahead
regressor (numpy.Array) – additional regressor, not used
just_point_forecast (bool) – If True, return a pandas.DataFrame of just point forecasts
- Returns
Either a PredictionObject of forecasts and metadata, or if just_point_forecast == True, a dataframe of point forecasts
-
class
autots.models.statsmodels.
UnobservedComponents
(name: str = 'UnobservedComponents', frequency: str = 'infer', prediction_interval: float = 0.9, regression_type: str = None, holiday_country: str = 'US', random_seed: int = 2020, verbose: int = 0, level: bool = False, trend: bool = False, cycle: bool = False, damped_cycle: bool = False, irregular: bool = False, stochastic_cycle: bool = False, stochastic_trend: bool = False, stochastic_level: bool = False, **kwargs)¶ Bases:
autots.evaluator.auto_model.ModelObject
UnobservedComponents from Statsmodels.
- Parameters
name (str) – String to identify class
frequency (str) – String alias of datetime index frequency or else ‘infer’
prediction_interval (float) – Confidence interval for probabilistic forecast
regression_type (str) – type of regression (None, ‘User’, or ‘Holiday’)
-
fit
(df, future_regressor=[])¶ Train algorithm given data supplied
- Parameters
df (pandas.DataFrame) – Datetime Indexed
-
get_new_params
(method: str = 'random')¶ Return dict of new parameters for parameter tuning.
-
get_params
()¶ Return dict of current parameters.
-
predict
(forecast_length: int, future_regressor=[], just_point_forecast: bool = False)¶ Generate forecast data immediately following dates of index supplied to .fit().
- Parameters
forecast_length (int) – Number of periods of data to forecast ahead
regressor (numpy.Array) – additional regressor, not used
just_point_forecast (bool) – If True, return a pandas.DataFrame of just point forecasts
- Returns
Either a PredictionObject of forecasts and metadata, or if just_point_forecast == True, a dataframe of point forecasts
-
class
autots.models.statsmodels.
VAR
(name: str = 'VAR', frequency: str = 'infer', prediction_interval: float = 0.9, regression_type: str = None, holiday_country: str = 'US', random_seed: int = 2020, verbose: int = 0, maxlags: int = 15, ic: str = 'fpe', **kwargs)¶ Bases:
autots.evaluator.auto_model.ModelObject
VAR from Statsmodels.
- Parameters
name (str) – String to identify class
frequency (str) – String alias of datetime index frequency or else ‘infer’
prediction_interval (float) – Confidence interval for probabilistic forecast
regression_type (str) – type of regression (None, ‘User’, or ‘Holiday’)
-
fit
(df, future_regressor=[])¶ Train algorithm given data supplied.
- Parameters
df (pandas.DataFrame) – Datetime Indexed
-
get_new_params
(method: str = 'random')¶ Return dict of new parameters for parameter tuning.
-
get_params
()¶ Return dict of current parameters.
-
predict
(forecast_length: int, future_regressor=[], just_point_forecast=False)¶ Generates forecast data immediately following dates of index supplied to .fit()
- Parameters
forecast_length (int) – Number of periods of data to forecast ahead
regressor (numpy.Array) – additional regressor, not used
just_point_forecast (bool) – If True, return a pandas.DataFrame of just point forecasts
- Returns
Either a PredictionObject of forecasts and metadata, or if just_point_forecast == True, a dataframe of point forecasts
-
class
autots.models.statsmodels.
VARMAX
(name: str = 'VARMAX', frequency: str = 'infer', prediction_interval: float = 0.9, regression_type: str = None, holiday_country: str = 'US', random_seed: int = 2020, verbose: int = 0, order: tuple = (1, 0), trend: str = 'c', **kwargs)¶ Bases:
autots.evaluator.auto_model.ModelObject
VARMAX from Statsmodels
- Parameters
name (str) – String to identify class
frequency (str) – String alias of datetime index frequency or else ‘infer’
prediction_interval (float) – Confidence interval for probabilistic forecast
regression_type (str) – type of regression (None, ‘User’, or ‘Holiday’)
-
fit
(df, future_regressor=[])¶ Train algorithm given data supplied
- Parameters
df (pandas.DataFrame) – Datetime Indexed
-
get_new_params
(method: str = 'random')¶ Return dict of new parameters for parameter tuning.
-
get_params
()¶ Return dict of current parameters.
-
predict
(forecast_length: int, future_regressor=[], just_point_forecast=False)¶ Generate forecast data immediately following dates of index supplied to .fit().
- Parameters
forecast_length (int) – Number of periods of data to forecast ahead
regressor (numpy.Array) – additional regressor, not used
just_point_forecast (bool) – If True, return a pandas.DataFrame of just point forecasts
- Returns
Either a PredictionObject of forecasts and metadata, or if just_point_forecast == True, a dataframe of point forecasts
-
class
autots.models.statsmodels.
VECM
(name: str = 'VECM', frequency: str = 'infer', prediction_interval: float = 0.9, regression_type: str = None, holiday_country: str = 'US', random_seed: int = 2020, verbose: int = 0, deterministic: str = 'nc', k_ar_diff: int = 1, **kwargs)¶ Bases:
autots.evaluator.auto_model.ModelObject
VECM from Statsmodels
- Parameters
name (str) – String to identify class
frequency (str) – String alias of datetime index frequency or else ‘infer’
prediction_interval (float) – Confidence interval for probabilistic forecast
regression_type (str) – type of regression (None, ‘User’, or ‘Holiday’)
-
fit
(df, future_regressor=[])¶ Train algorithm given data supplied.
- Parameters
df (pandas.DataFrame) – Datetime Indexed
-
get_new_params
(method: str = 'random')¶ Return dict of new parameters for parameter tuning.
-
get_params
()¶ Return dict of current parameters.
-
predict
(forecast_length: int, future_regressor=[], just_point_forecast=False)¶ Generates forecast data immediately following dates of index supplied to .fit()
- Parameters
forecast_length (int) – Number of periods of data to forecast ahead
regressor (numpy.Array) – additional regressor, not used
just_point_forecast (bool) – If True, return a pandas.DataFrame of just point forecasts
- Returns
Either a PredictionObject of forecasts and metadata, or if just_point_forecast == True, a dataframe of point forecasts
autots.models.tfp module¶
-
class
autots.models.tfp.
TFPRegression
(name: str = 'TFPRegression', frequency: str = 'infer', prediction_interval: float = 0.9, holiday_country: str = 'US', random_seed: int = 2020, verbose: int = 1, kernel_initializer: str = 'lecun_uniform', optimizer: str = 'adam', loss: str = 'negloglike', epochs: int = 50, batch_size: int = 32, dist: str = 'normal', regression_type: str = None)¶ Bases:
autots.evaluator.auto_model.ModelObject
Tensorflow Probability regression.
- Parameters
name (str) – String to identify class
frequency (str) – String alias of datetime index frequency or else ‘infer’
prediction_interval (float) – Confidence interval for probabilistic forecast
regression_type (str) – type of regression (None, ‘User’)
-
fit
(df, future_regressor=[])¶ Train algorithm given data supplied
- Parameters
df (pandas.DataFrame) – Datetime Indexed
-
get_new_params
(method: str = 'random')¶ Return dict of new parameters for parameter tuning.
-
get_params
()¶ Return dict of current parameters.
-
predict
(forecast_length: int, future_regressor=[], just_point_forecast=False)¶ Generates forecast data immediately following dates of index supplied to .fit()
- Parameters
forecast_length (int) – Number of periods of data to forecast ahead
regressor (numpy.Array) – additional regressor, not used
just_point_forecast (bool) – If True, return a pandas.DataFrame of just point forecasts
- Returns
Either a PredictionObject of forecasts and metadata, or if just_point_forecast == True, a dataframe of point forecasts
-
class
autots.models.tfp.
TFPRegressor
(kernel_initializer: str = 'lecun_uniform', optimizer: str = 'adam', loss: str = 'negloglike', epochs: int = 50, batch_size: int = 32, dist: str = 'normal', verbose: int = 1, random_seed: int = 2020)¶ Bases:
object
Wrapper for Tensorflow Keras based RNN.
- Parameters
rnn_type (str) – Keras cell type ‘GRU’ or default ‘LSTM’
kernel_initializer (str) – passed to first keras LSTM or GRU layer
hidden_layer_sizes (tuple) – of len 1 or 3 passed to first keras LSTM or GRU layers
optimizer (str) – Passed to keras model.compile
loss (str) – Passed to keras model.compile
epochs (int) – Passed to keras model.fit
batch_size (int) – Passed to keras model.fit
verbose (int) – 0, 1 or 2. Passed to keras model.fit
random_seed (int) – passed to tf.random.set_seed()
-
fit
(X, Y)¶ Train the model on dataframes of X and Y.
-
predict
(X, conf_int: float = None)¶ Predict on dataframe of X.
-
class
autots.models.tfp.
TensorflowSTS
(name: str = 'TensorflowSTS', frequency: str = 'infer', prediction_interval: float = 0.9, regression_type: str = None, holiday_country: str = 'US', random_seed: int = 2020, verbose: int = 0, trend: str = 'local', seasonal_periods: int = None, ar_order: int = None, fit_method: str = 'hmc', num_steps: int = 200)¶ Bases:
autots.evaluator.auto_model.ModelObject
STS from TensorflowProbability.
- Parameters
name (str) – String to identify class
frequency (str) – String alias of datetime index frequency or else ‘infer’
prediction_interval (float) – Confidence interval for probabilistic forecast
regression_type (str) – type of regression (None, ‘User’, or ‘Holiday’)
-
fit
(df, future_regressor=[])¶ Train algorithm given data supplied.
- Parameters
df (pandas.DataFrame) – Datetime Indexed
-
get_new_params
(method: str = 'random')¶ Return dict of new parameters for parameter tuning.
-
get_params
()¶ Return dict of current parameters.
-
predict
(forecast_length: int, future_regressor=[], just_point_forecast=False)¶ Generates forecast data immediately following dates of index supplied to .fit()
- Parameters
forecast_length (int) – Number of periods of data to forecast ahead
regressor (numpy.Array) – additional regressor, not used
just_point_forecast (bool) – If True, return a pandas.DataFrame of just point forecasts
- Returns
Either a PredictionObject of forecasts and metadata, or if just_point_forecast == True, a dataframe of point forecasts
autots.models.tsfresh module¶
tsfresh - automated feature extraction
n_jobs>1 causes Windows issues, sometimes maybe
-
class
autots.models.tsfresh.
TSFreshRegressor
(name: str = 'TSFreshRegressor', frequency: str = 'infer', prediction_interval: float = 0.9, regression_type: str = None, holiday_country: str = 'US', verbose: int = 0, random_seed: int = 2020, regression_model: str = 'Adaboost', max_timeshift: int = 10, feature_selection: str = None)¶ Bases:
autots.evaluator.auto_model.ModelObject
Sklearn + TSFresh feature generation
- Parameters
name (str) – String to identify class
frequency (str) – String alias of datetime index frequency or else ‘infer’
prediction_interval (float) – Confidence interval for probabilistic forecast
holiday (bool) – If true, include holiday flags
regression_type (str) – type of regression (None, ‘User’)
-
fit
(df, future_regressor=[])¶ Train algorithm given data supplied
- Parameters
df (pandas.DataFrame) – Datetime Indexed
-
get_new_params
(method: str = 'random')¶ Returns dict of new parameters for parameter tuning
-
get_params
()¶ Return dict of current parameters
-
predict
(forecast_length: int, future_regressor=[], just_point_forecast: bool = False)¶ Generates forecast data immediately following dates of index supplied to .fit()
- Parameters
forecast_length (int) – Number of periods of data to forecast ahead
regressor (numpy.Array) – additional regressor
just_point_forecast (bool) – If True, return a pandas.DataFrame of just point forecasts
- Returns
Either a PredictionObject of forecasts and metadata, or if just_point_forecast == True, a dataframe of point forecasts
Module contents¶
Model Models