etna.models.BATSModel#

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

Bases: PerSegmentModelMixin, PredictionIntervalContextIgnorantModelMixin, PredictionIntervalContextIgnorantAbstractModel

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

Methods

fit(ts)

Fit model.

forecast(ts[, prediction_interval, ...])

Make predictions.

get_model()

Get internal models that are used inside etna class.

load(path)

Load an object.

params_to_tune()

Get grid for tuning hyperparameters.

predict(ts[, prediction_interval, ...])

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

save(path)

Save the object.

set_params(**params)

Return new object instance with modified parameters.

to_dict()

Collect all information about etna object in dict.

Attributes

This class stores its __init__ parameters as attributes.

context_size

Context size of the model.
fit(ts: TSDataset) → PerSegmentModelMixin[source]#

Fit model.

Parameters:

ts (TSDataset) – Dataset with features

Returns:

Model after fit

Return type:

PerSegmentModelMixin

forecast(ts: TSDataset, prediction_interval: bool = False, quantiles: Sequence[float] = (0.025, 0.975), return_components: bool = False) → TSDataset[source]#

Make predictions.

Parameters:

  • ts (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:

TSDataset

get_model() → Dict[str, Any][source]#

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: Path) → Self[source]#

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 (Path) – Path to load object from.

Returns:

Loaded object.

Return type:

Self

params_to_tune() → Dict[str, BaseDistribution][source]#

Get grid for tuning hyperparameters.

This is default implementation with empty grid.

Returns:

Empty grid.

Return type:

Dict[str, BaseDistribution]

predict(ts: TSDataset, prediction_interval: bool = False, quantiles: Sequence[float] = (0.025, 0.975), return_components: bool = False) → TSDataset[source]#

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

Parameters:

  • ts (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:

TSDataset

save(path: Path)[source]#

Save the object.

Parameters:

path (Path) – Path to save object to.

set_params(**params: dict) → Self[source]#

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 (dict) – Estimator parameters

Returns:

New instance with changed parameters

Return type:

Self

Examples

>>> from etna.pipeline import Pipeline
>>> from etna.models import NaiveModel
>>> from etna.transforms import AddConstTransform
>>> 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()[source]#

Collect all information about etna object in dict.

property context_size: int[source]#

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

Zero for this model.