ThetaForecaster¶
-
class
sktime.forecasting.theta.
ThetaForecaster
(initial_level=None, deseasonalize=True, sp=1)[source]¶ Theta method of forecasting.
The theta method as defined in 1 is equivalent to simple exponential smoothing (SES) with drift. This is demonstrated in 2.
The series is tested for seasonality using the test outlined in A&N. If deemed seasonal, the series is seasonally adjusted using a classical multiplicative decomposition before applying the theta method. The resulting forecasts are then reseasonalised.
In cases where SES results in a constant forecast, the theta forecaster will revert to predicting the SES constant plus a linear trend derived from the training data.
Prediction intervals are computed using the underlying state space model.
- Parameters
initial_level (float, optional) – The alpha value of the simple exponential smoothing, if the value is set then this will be used, otherwise it will be estimated from the data.
deseasonalize (bool, optional (default=True)) – If True, data is seasonally adjusted.
sp (int, optional (default=1)) – The number of observations that constitute a seasonal period for a multiplicative deseasonaliser, which is used if seasonality is detected in the training data. Ignored if a deseasonaliser transformer is provided. Default is 1 (no seasonality).
References
- decomposition
approach to forecasting. International Journal of Forecasting 16, 521-530, 2000. <https://www.sciencedirect.com/science/article/pii /S0169207000000662>`_