StatsForecastAutoARIMAModel¶

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]¶

Bases: etna.models.mixins.PerSegmentModelMixin, etna.models.mixins.PredictionIntervalContextIgnorantModelMixin, etna.models.base.PredictionIntervalContextIgnorantAbstractModel

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.

Inherited-members

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

context_size

Context size of the 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.