_DeadlineMovingAverageModel

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

Bases: object

Moving average model that uses exact previous dates to predict.

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 monthly or annual seasonality.

Inherited-members

Methods

fit(df, regressors)

Fit DeadlineMovingAverageModel model.

forecast(df, prediction_size)

Compute autoregressive forecasts.

predict(df, prediction_size)

Compute predictions using true target data as context.

Attributes

context_size

Upper bound to context size of the model.
fit(df: pandas.core.frame.DataFrame, regressors: List[str]) → etna.models.deadline_ma._DeadlineMovingAverageModel[source]

Fit DeadlineMovingAverageModel model.

Parameters

  • df (pd.DataFrame) – Data to fit on

  • regressors (List[str]) – List of the columns with regressors(ignored in this model)

Raises

  • ValueError – If freq of dataframe is not supported

  • ValueError – If series is too short for chosen shift value

Returns

Fitted model

Return type

etna.models.deadline_ma._DeadlineMovingAverageModel

forecast(df: pandas.core.frame.DataFrame, prediction_size: int) → numpy.ndarray[source]

Compute autoregressive forecasts.

Parameters

  • df (pandas.core.frame.DataFrame) – Features dataframe.

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

Returns

Array with predictions.

Raises

  • ValueError: – if context isn’t big enough

  • ValueError: – if forecast context contains NaNs

Return type

numpy.ndarray

predict(df: pandas.core.frame.DataFrame, prediction_size: int) → numpy.ndarray[source]

Compute predictions using true target data as context.

Parameters

  • df (pandas.core.frame.DataFrame) – Features dataframe.

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

Returns

Array with predictions.

Raises

  • ValueError: – if context isn’t big enough

  • ValueError: – if there are NaNs in a target column on timestamps that are required to make predictions

Return type

numpy.ndarray

property context_size: int

Upper bound to context size of the model.