statsforecast

Classes

StatsForecastARIMAModel([order, ...])

Class for holding statsforecast.models.ARIMA.

StatsForecastAutoARIMAModel([d, D, max_p, ...])

Class for holding statsforecast.models.AutoARIMA.

StatsForecastAutoCESModel([season_length, model])

Class for holding statsforecast.models.AutoCES.

StatsForecastAutoETSModel([season_length, ...])

Class for holding statsforecast.models.AutoETS.

StatsForecastAutoThetaModel([season_length, ...])

Class for holding statsforecast.models.AutoTheta.

_ARIMAAdapter([order, season_length, ...])

Adapter for statsforecast.models.ARIMA.

_AutoARIMAAdapter([d, D, max_p, max_q, ...])

Adapter for statsforecast.models.AutoARIMA.

_AutoCESAdapter([season_length, model])

Adapter for statsforecast.models.AutoCES.

_AutoETSAdapter([season_length, model, damped])

Adapter for statsforecast.models.AutoETS.

_AutoThetaAdapter([season_length, ...])

Adapter for statsforecast.models.AutoTheta.

_StatsForecastBaseAdapter(model, ...)

Base class for adapters for models from statsforecast package.
class StatsForecastARIMAModel(order: Tuple[int, int, int] = (0, 0, 0), season_length: int = 1, seasonal_order: Tuple[int, int, int] = (0, 0, 0), **kwargs)[source]

Class for holding statsforecast.models.ARIMA.

Documentation for the underlying model.

Init model with given params.

Parameters

  • order (Tuple[int, int, int]) – A specification of the non-seasonal part of the ARIMA model: the three components (p, d, q) are the AR order, the degree of differencing, and the MA order.

  • season_length (int) – Number of observations per unit of time. Ex: 24 Hourly data.

  • seasonal_order (Tuple[int, int, int]) – A specification of the seasonal part of the ARIMA model. (P, D, Q) for the AR order, the degree of differencing, the MA order.

  • **kwargs – Additional parameters for statsforecast.models.ARIMA.

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][source]

Get default grid for tuning hyperparameters.

This grid tunes parameters: order.0, order.1, order.2. If self.season_length is greater than one, then it also tunes parameters: seasonal_order.0, seasonal_order.1, seasonal_order.2. Other parameters are expected to be set by the user.

Returns

Grid to tune.

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 StatsForecastAutoARIMAModel(d: Optional[int] = None, D: Optional[int] = None, max_p: int = 5, max_q: int = 5, max_P: int = 2, max_Q: int = 2, max_order: int = 5, max_d: int = 2, max_D: int = 1, start_p: int = 2, start_q: int = 2, start_P: int = 1, start_Q: int = 1, season_length: int = 1, **kwargs)[source]

Class for holding statsforecast.models.AutoARIMA.

Documentation for the underlying model.

Init model with given params.

Parameters

  • d (Optional[int]) – Order of first-differencing.

  • D (Optional[int]) – Order of seasonal-differencing.

  • max_p (int) – Max autorregresives p.

  • max_q (int) – Max moving averages q.

  • max_P (int) – Max seasonal autorregresives P.

  • max_Q (int) – Max seasonal moving averages Q.

  • max_order (int) – Max p+q+P+Q value if not stepwise selection.

  • max_d (int) – Max non-seasonal differences.

  • max_D (int) – Max seasonal differences.

  • start_p (int) – Starting value of p in stepwise procedure.

  • start_q (int) – Starting value of q in stepwise procedure.

  • start_P (int) – Starting value of P in stepwise procedure.

  • start_Q (int) – Starting value of Q in stepwise procedure.

  • season_length (int) – Number of observations per unit of time. Ex: 24 Hourly data.

  • **kwargs – Additional parameters for statsforecast.models.AutoARIMA.

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 StatsForecastAutoCESModel(season_length: int = 1, model: str = 'Z')[source]

Class for holding statsforecast.models.AutoCES.

Documentation for the underlying model.

Init model with given params.

Parameters

  • season_length (int) – Number of observations per unit of time. Ex: 24 Hourly data.

  • model (str) – Controlling state-space-equations.

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, return_components: bool = False) → etna.datasets.tsdataset.TSDataset

Make predictions.

Parameters
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, return_components: bool = False) → etna.datasets.tsdataset.TSDataset

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

Parameters
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 StatsForecastAutoETSModel(season_length: int = 1, model: str = 'ZZZ', damped: Optional[bool] = None)[source]

Class for holding statsforecast.models.AutoETS.

Documentation for the underlying model.

Init model with given params.

Parameters

  • season_length (int) – Number of observations per unit of time. Ex: 24 Hourly data.

  • model (str) – Controlling state-space-equations.

  • damped (Optional[bool]) – A parameter that ‘dampens’ the trend.

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 StatsForecastAutoThetaModel(season_length: int = 1, decomposition_type: str = 'multiplicative', model: Optional[str] = None)[source]

Class for holding statsforecast.models.AutoTheta.

Documentation for the underlying model.

Init model with given params.

Parameters

  • season_length (int) – Number of observations per unit of time. Ex: 24 Hourly data.

  • decomposition_type (str) – Sesonal decomposition type, ‘multiplicative’ (default) or ‘additive’.

  • model (Optional[str]) – Controlling Theta Model. By default searches the best model.

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.