SeasonalMovingAverageModel

class SeasonalMovingAverageModel(window: int = 5, seasonality: int = 7)[source]

Bases: etna.models.mixins.PerSegmentModelMixin, etna.models.mixins.NonPredictionIntervalContextRequiredModelMixin, etna.models.base.NonPredictionIntervalContextRequiredAbstractModel

Seasonal moving average.

\[y_{t} = \frac{\sum_{i=1}^{n} y_{t-is} }{n},\]

where \(s\) is seasonality, \(n\) is window size (how many history values are taken for forecast).

Initialize seasonal moving average model.

Length of the context is window * seasonality.

Parameters

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

  • seasonality (int) – Lag between values taken for forecast.

Inherited-members

Methods

fit(ts)

Fit model.

forecast(ts, prediction_size)

Make predictions.

get_model()

Get internal model.

load(path)

Load an object.

predict(ts, prediction_size)

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.
get_model() → Dict[str, etna.models.seasonal_ma.SeasonalMovingAverageModel][source]

Get internal model.

Returns

Internal model

Return type

Dict[str, etna.models.seasonal_ma.SeasonalMovingAverageModel]

property context_size: int

Context size of the model.