tbats

Classes

BATSModel([use_box_cox, box_cox_bounds, ...])

Class for holding segment interval BATS model.

TBATSModel([use_box_cox, box_cox_bounds, ...])

Class for holding segment interval TBATS model.

_TBATSAdapter(model)

class BATSModel(use_box_cox: Optional[bool] = None, box_cox_bounds: Tuple[int, int] = (0, 1), use_trend: Optional[bool] = None, use_damped_trend: Optional[bool] = None, seasonal_periods: Optional[Iterable[int]] = None, use_arma_errors: bool = True, show_warnings: bool = True, n_jobs: Optional[int] = None, multiprocessing_start_method: str = 'spawn', context: Optional[tbats.abstract.ContextInterface.ContextInterface] = None)[source]

Class for holding segment interval BATS model.

Notes

This model supports in-sample and out-of-sample prediction decomposition. Prediction components for BATS model are: local level, trend, seasonality and ARMA component. In-sample and out-of-sample decompositions components are estimated directly from the fitted model parameters. Box-Cox transform supported with components proportional rescaling.

Create BATSModel with given parameters.

Parameters

  • use_box_cox (bool or None, optional (default=None)) – If Box-Cox transformation of original series should be applied. When None both cases shall be considered and better is selected by AIC.

  • box_cox_bounds (tuple, shape=(2,), optional (default=(0, 1))) – Minimal and maximal Box-Cox parameter values.

  • use_trend (bool or None, optional (default=None)) – Indicates whether to include a trend or not. When None both cases shall be considered and better is selected by AIC.

  • use_damped_trend (bool or None, optional (default=None)) – Indicates whether to include a damping parameter in the trend or not. Applies only when trend is used. When None both cases shall be considered and better is selected by AIC.

  • seasonal_periods (iterable or array-like of int values, optional (default=None)) – Length of each of the periods (amount of observations in each period). BATS accepts only int values here. When None or empty array, non-seasonal model shall be fitted.

  • use_arma_errors (bool, optional (default=True)) – When True BATS will try to improve the model by modelling residuals with ARMA. Best model will be selected by AIC. If False, ARMA residuals modeling will not be considered.

  • show_warnings (bool, optional (default=True)) – If warnings should be shown or not. Also see Model.warnings variable that contains all model related warnings.

  • n_jobs (int, optional (default=None)) – How many jobs to run in parallel when fitting BATS model. When not provided BATS shall try to utilize all available cpu cores.

  • multiprocessing_start_method (str, optional (default='spawn')) – How threads should be started. See https://docs.python.org/3/library/multiprocessing.html#contexts-and-start-methods

  • context (abstract.ContextInterface, optional (default=None)) – For advanced users only. Provide this to override default behaviors

fit(ts: etna.datasets.tsdataset.TSDataset) → etna.models.mixins.PerSegmentModelMixin

Fit model.

Parameters

ts (etna.datasets.tsdataset.TSDataset) – Dataset with features

Returns

Model after fit

Return type

etna.models.mixins.PerSegmentModelMixin

forecast(ts: etna.datasets.tsdataset.TSDataset, prediction_interval: bool = False, quantiles: Sequence[float] = (0.025, 0.975), return_components: bool = False) → etna.datasets.tsdataset.TSDataset

Make predictions.

Parameters

  • ts (etna.datasets.tsdataset.TSDataset) – Dataset with features

  • prediction_interval (bool) – If True returns prediction interval for forecast

  • quantiles (Sequence[float]) – Levels of prediction distribution. By default 2.5% and 97.5% are taken to form a 95% prediction interval

  • return_components (bool) – If True additionally returns forecast components

Returns

Dataset with predictions

Return type

etna.datasets.tsdataset.TSDataset

get_model() → Dict[str, Any]

Get internal models that are used inside etna class.

Internal model is a model that is used inside etna to forecast segments, e.g. catboost.CatBoostRegressor or sklearn.linear_model.Ridge.

Returns

dictionary where key is segment and value is internal model

Return type

Dict[str, Any]

classmethod load(path: pathlib.Path) → typing_extensions.Self

Load an object.

Warning

This method uses dill module which is not secure. It is possible to construct malicious data which will execute arbitrary code during loading. Never load data that could have come from an untrusted source, or that could have been tampered with.

Parameters

path (pathlib.Path) – Path to load object from.

Returns

Loaded object.

Return type

typing_extensions.Self

params_to_tune() → Dict[str, etna.distributions.distributions.BaseDistribution]

Get grid for tuning hyperparameters.

This is default implementation with empty grid.

Returns

Empty grid.

Return type

Dict[str, etna.distributions.distributions.BaseDistribution]

predict(ts: etna.datasets.tsdataset.TSDataset, prediction_interval: bool = False, quantiles: Sequence[float] = (0.025, 0.975), return_components: bool = False) → etna.datasets.tsdataset.TSDataset

Make predictions with using true values as autoregression context if possible (teacher forcing).

Parameters

  • ts (etna.datasets.tsdataset.TSDataset) – Dataset with features

  • prediction_interval (bool) – If True returns prediction interval for forecast

  • quantiles (Sequence[float]) – Levels of prediction distribution. By default 2.5% and 97.5% are taken to form a 95% prediction interval

  • return_components (bool) – If True additionally returns prediction components

Returns

Dataset with predictions

Return type

etna.datasets.tsdataset.TSDataset

save(path: pathlib.Path)

Save the object.

Parameters

path (pathlib.Path) – Path to save object to.

set_params(**params: dict) → etna.core.mixins.TMixin

Return new object instance with modified parameters.

Method also allows to change parameters of nested objects within the current object. For example, it is possible to change parameters of a model in a Pipeline.

Nested parameters are expected to be in a <component_1>.<...>.<parameter> form, where components are separated by a dot.

Parameters

  • **params – Estimator parameters

  • self (etna.core.mixins.TMixin) –

  • params (dict) –

Returns

New instance with changed parameters

Return type

etna.core.mixins.TMixin

Examples

>>> from etna.pipeline import Pipeline
>>> from etna.models import NaiveModel
>>> from etna.transforms import AddConstTransform
>>> model = model=NaiveModel(lag=1)
>>> transforms = [AddConstTransform(in_column="target", value=1)]
>>> pipeline = Pipeline(model, transforms=transforms, horizon=3)
>>> pipeline.set_params(**{"model.lag": 3, "transforms.0.value": 2})
Pipeline(model = NaiveModel(lag = 3, ), transforms = [AddConstTransform(in_column = 'target', value = 2, inplace = True, out_column = None, )], horizon = 3, )
to_dict()

Collect all information about etna object in dict.

property context_size: int

Context size of the model. Determines how many history points do we ask to pass to the model.

Zero for this model.

class TBATSModel(use_box_cox: Optional[bool] = None, box_cox_bounds: Tuple[int, int] = (0, 1), use_trend: Optional[bool] = None, use_damped_trend: Optional[bool] = None, seasonal_periods: Optional[Iterable[int]] = None, use_arma_errors: bool = True, show_warnings: bool = True, n_jobs: Optional[int] = None, multiprocessing_start_method: str = 'spawn', context: Optional[tbats.abstract.ContextInterface.ContextInterface] = None)[source]

Class for holding segment interval TBATS model.

Notes

This model supports in-sample and out-of-sample prediction decomposition. Prediction components for TBATS model are: local level, trend, seasonality and ARMA component. In-sample and out-of-sample decompositions components are estimated directly from the fitted model parameters. Box-Cox transform supported with components proportional rescaling.

Create TBATSModel with given parameters.

Parameters

  • use_box_cox (bool or None, optional (default=None)) – If Box-Cox transformation of original series should be applied. When None both cases shall be considered and better is selected by AIC.

  • box_cox_bounds (tuple, shape=(2,), optional (default=(0, 1))) – Minimal and maximal Box-Cox parameter values.

  • use_trend (bool or None, optional (default=None)) – Indicates whether to include a trend or not. When None both cases shall be considered and better is selected by AIC.

  • use_damped_trend (bool or None, optional (default=None)) – Indicates whether to include a damping parameter in the trend or not. Applies only when trend is used. When None both cases shall be considered and better is selected by AIC.

  • seasonal_periods (iterable or array-like of floats, optional (default=None)) – Length of each of the periods (amount of observations in each period). TBATS accepts int and float values here. When None or empty array, non-seasonal model shall be fitted.

  • use_arma_errors (bool, optional (default=True)) – When True BATS will try to improve the model by modelling residuals with ARMA. Best model will be selected by AIC. If False, ARMA residuals modeling will not be considered.

  • show_warnings (bool, optional (default=True)) – If warnings should be shown or not. Also see Model.warnings variable that contains all model related warnings.

  • n_jobs (int, optional (default=None)) – How many jobs to run in parallel when fitting BATS model. When not provided BATS shall try to utilize all available cpu cores.

  • multiprocessing_start_method (str, optional (default='spawn')) – How threads should be started. See https://docs.python.org/3/library/multiprocessing.html#contexts-and-start-methods

  • context (abstract.ContextInterface, optional (default=None)) – For advanced users only. Provide this to override default behaviors

fit(ts: etna.datasets.tsdataset.TSDataset) → etna.models.mixins.PerSegmentModelMixin

Fit model.

Parameters

ts (etna.datasets.tsdataset.TSDataset) – Dataset with features

Returns

Model after fit

Return type

etna.models.mixins.PerSegmentModelMixin

forecast(ts: etna.datasets.tsdataset.TSDataset, prediction_interval: bool = False, quantiles: Sequence[float] = (0.025, 0.975), return_components: bool = False) → etna.datasets.tsdataset.TSDataset

Make predictions.

Parameters

  • ts (etna.datasets.tsdataset.TSDataset) – Dataset with features

  • prediction_interval (bool) – If True returns prediction interval for forecast

  • quantiles (Sequence[float]) – Levels of prediction distribution. By default 2.5% and 97.5% are taken to form a 95% prediction interval

  • return_components (bool) – If True additionally returns forecast components

Returns

Dataset with predictions

Return type

etna.datasets.tsdataset.TSDataset

get_model() → Dict[str, Any]

Get internal models that are used inside etna class.

Internal model is a model that is used inside etna to forecast segments, e.g. catboost.CatBoostRegressor or sklearn.linear_model.Ridge.

Returns

dictionary where key is segment and value is internal model

Return type

Dict[str, Any]

classmethod load(path: pathlib.Path) → typing_extensions.Self

Load an object.

Warning

This method uses dill module which is not secure. It is possible to construct malicious data which will execute arbitrary code during loading. Never load data that could have come from an untrusted source, or that could have been tampered with.

Parameters

path (pathlib.Path) – Path to load object from.

Returns

Loaded object.

Return type

typing_extensions.Self

params_to_tune() → Dict[str, etna.distributions.distributions.BaseDistribution]

Get grid for tuning hyperparameters.

This is default implementation with empty grid.

Returns

Empty grid.

Return type

Dict[str, etna.distributions.distributions.BaseDistribution]

predict(ts: etna.datasets.tsdataset.TSDataset, prediction_interval: bool = False, quantiles: Sequence[float] = (0.025, 0.975), return_components: bool = False) → etna.datasets.tsdataset.TSDataset

Make predictions with using true values as autoregression context if possible (teacher forcing).

Parameters

  • ts (etna.datasets.tsdataset.TSDataset) – Dataset with features

  • prediction_interval (bool) – If True returns prediction interval for forecast

  • quantiles (Sequence[float]) – Levels of prediction distribution. By default 2.5% and 97.5% are taken to form a 95% prediction interval

  • return_components (bool) – If True additionally returns prediction components

Returns

Dataset with predictions

Return type

etna.datasets.tsdataset.TSDataset

save(path: pathlib.Path)

Save the object.

Parameters

path (pathlib.Path) – Path to save object to.

set_params(**params: dict) → etna.core.mixins.TMixin

Return new object instance with modified parameters.

Method also allows to change parameters of nested objects within the current object. For example, it is possible to change parameters of a model in a Pipeline.

Nested parameters are expected to be in a <component_1>.<...>.<parameter> form, where components are separated by a dot.

Parameters

  • **params – Estimator parameters

  • self (etna.core.mixins.TMixin) –

  • params (dict) –

Returns

New instance with changed parameters

Return type

etna.core.mixins.TMixin

Examples

>>> from etna.pipeline import Pipeline
>>> from etna.models import NaiveModel
>>> from etna.transforms import AddConstTransform
>>> model = model=NaiveModel(lag=1)
>>> transforms = [AddConstTransform(in_column="target", value=1)]
>>> pipeline = Pipeline(model, transforms=transforms, horizon=3)
>>> pipeline.set_params(**{"model.lag": 3, "transforms.0.value": 2})
Pipeline(model = NaiveModel(lag = 3, ), transforms = [AddConstTransform(in_column = 'target', value = 2, inplace = True, out_column = None, )], horizon = 3, )
to_dict()

Collect all information about etna object in dict.

property context_size: int

Context size of the model. Determines how many history points do we ask to pass to the model.

Zero for this model.