PredictionIntervalContextIgnorantAbstractModel

class PredictionIntervalContextIgnorantAbstractModel[source]

Bases: etna.models.base.AbstractModel

Interface for models that support prediction intervals and don’t need context for prediction.

Inherited-members

Methods

fit(ts)

Fit model.

forecast(ts[, prediction_interval, quantiles])

Make predictions.

get_model()

Get internal model/models that are used inside etna class.

load(path)

Load an object.

predict(ts[, prediction_interval, quantiles])

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

save(path)

Save the object.

to_dict()

Collect all information about etna object in dict.

Attributes

context_size

Context size of the model.
abstract forecast(ts: etna.datasets.tsdataset.TSDataset, prediction_interval: bool = False, quantiles: Sequence[float] = (0.025, 0.975)) → etna.datasets.tsdataset.TSDataset[source]

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

Returns

Dataset with predictions

Return type

etna.datasets.tsdataset.TSDataset

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

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

Returns

Dataset with predictions

Return type

etna.datasets.tsdataset.TSDataset

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.