DeadlineMovingAverageModel

class DeadlineMovingAverageModel(window: int = 3, seasonality: str = 'month')[source]

Bases: etna.models.base.NonPredictionIntervalContextRequiredAbstractModel

Moving average model that uses exact previous dates to predict.

Notes

This model supports in-sample and out-of-sample prediction decomposition. Prediction components are corresponding target seasonal lags (monthly or annual) with weights of \(1/window\).

Initialize deadline moving average model.

Length of the context is equal to the number of window months or years, depending on the seasonality.

Parameters

  • window (int) – Number of values taken for forecast for each point.

  • seasonality (str) – Only allowed values are “month” and “year”.

Inherited-members

Methods

fit(ts)

Fit model.

forecast(ts, prediction_size[, ...])

Make autoregressive forecasts.

get_model()

Get internal model.

load(path)

Load an object.

params_to_tune()

Get default grid for tuning hyperparameters.

predict(ts, prediction_size[, return_components])

Make predictions using true values as autoregression context (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

Upper bound to context size of the model.
fit(ts: etna.datasets.tsdataset.TSDataset) → etna.models.deadline_ma.DeadlineMovingAverageModel[source]

Fit model.

Parameters

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

Returns

Model after fit

Return type

etna.models.deadline_ma.DeadlineMovingAverageModel

forecast(ts: etna.datasets.tsdataset.TSDataset, prediction_size: int, return_components: bool = False) → etna.datasets.tsdataset.TSDataset[source]

Make autoregressive forecasts.

Parameters

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

  • prediction_size (int) – Number of last timestamps to leave after making prediction. Previous timestamps will be used as a context.

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

Returns

Dataset with predictions

Raises

  • NotImplementedError: – if return_components mode is used

  • ValueError: – if model isn’t fitted

  • ValueError: – if context isn’t big enough

  • ValueError: – if forecast context contains NaNs

Return type

etna.datasets.tsdataset.TSDataset

get_model() → etna.models.deadline_ma.DeadlineMovingAverageModel[source]

Get internal model.

Returns

Itself

Return type

etna.models.deadline_ma.DeadlineMovingAverageModel

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

Get default grid for tuning hyperparameters.

This grid tunes window parameter. 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_size: int, return_components: bool = False) → etna.datasets.tsdataset.TSDataset[source]

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

Parameters

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

  • prediction_size (int) – Number of last timestamps to leave after making prediction. Previous timestamps will be used as a context.

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

Returns

Dataset with predictions

Raises

  • NotImplementedError: – if return_components mode is used

  • ValueError: – if model isn’t fitted

  • ValueError: – if context isn’t big enough

  • ValueError: – if forecast context contains NaNs

Return type

etna.datasets.tsdataset.TSDataset

property context_size: int

Upper bound to context size of the model.