_SeasonalMovingAverageModel¶
- class _SeasonalMovingAverageModel(window: int = 5, seasonality: int = 7)[source]¶
Bases:
objectSeasonal 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 remembered tail of series 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(df, regressors)Fit SeasonalMovingAverage model.
predict(df)Compute predictions from a SeasonalMovingAverage model.
- fit(df: pandas.core.frame.DataFrame, regressors: List[str]) etna.models.seasonal_ma._SeasonalMovingAverageModel[source]¶
Fit SeasonalMovingAverage model.
- Parameters
df (pd.DataFrame) – Data to fit on
regressors (List[str]) – List of the columns with regressors(ignored in this model)
- Returns
Fitted model
- Return type