# statsmodels.tsa.forecasting.theta.ThetaModel¶

class statsmodels.tsa.forecasting.theta.ThetaModel(endog, *, period: Optional[int] = None, deseasonalize: bool = True, use_test: bool = True, method: str = 'auto', difference: bool = False)[source]

The Theta forecasting model of Assimakopoulos and Nikolopoulos (2000)

Parameters
endogarray_like, 1d

The data to forecast.

periodint, default None

The period of the data that is used in the seasonality test and adjustment. If None then the period is determined from y’s index, if available.

deseasonalizebool, default True

A flag indicating whether the deseasonalize the data. If True and use_test is True, the data is only deseasonalized if the null of no seasonal component is rejected.

use_testbool, default True

A flag indicating whether test the period-th autocorrelation. If this test rejects using a size of 10%, then decomposition is used. Set to False to skip the test.

method{“auto”, “additive”, “multiplicative”}, default “auto”

The model used for the seasonal decomposition. “auto” uses a multiplicative if y is non-negative and all estimated seasonal components are positive. If either of these conditions is False, then it uses an additive decomposition.

differencebool, default False

A flag indicating to difference the data before testing for seasonality.

statsmodels.tsa.statespace.exponential_smoothing.ExponentialSmoothing

Exponential smoothing parameter estimation and forecasting

statsmodels.tsa.statespace.sarimax.SARIMAX

Seasonal ARIMA parameter estimation and forecasting

Notes

The Theta model forecasts the future as a weighted combination of two Theta lines. This class supports combinations of models with two thetas: 0 and a user-specified choice (default 2). The forecasts are then

$\hat{X}_{T+h|T} = \frac{\theta-1}{\theta} b_0 \left[h - 1 + \frac{1}{\alpha} - \frac{(1-\alpha)^T}{\alpha} \right] + \tilde{X}_{T+h|T}$

where $$\tilde{X}_{T+h|T}$$ is the SES forecast of the endogenous variable using the parameter $$\alpha$$. $$b_0$$ is the slope of a time trend line fitted to X using the terms 0, 1, …, T-1.

The model is estimated in steps:

1. Test for seasonality

2. Deseasonalize if seasonality detected

3. Estimate $$\alpha$$ by fitting a SES model to the data and $$b_0$$ by OLS.

4. Forecast the series

5. Reseasonalize if the data was deseasonalized.

The seasonality test examines where the autocorrelation at the seasonal period is different from zero. The seasonality is then removed using a seasonal decomposition with a multiplicative trend. If the seasonality estimate is non-positive then an additive trend is used instead. The default deseasonalizing method can be changed using the options.

References

1

Assimakopoulos, V., & Nikolopoulos, K. (2000). The theta model: a decomposition approach to forecasting. International Journal of Forecasting, 16(4), 521-530.

2

Hyndman, R. J., & Billah, B. (2003). Unmasking the Theta method. International Journal of Forecasting, 19(2), 287-290.

3

Fioruci, J. A., Pellegrini, T. R., Louzada, F., & Petropoulos, F. (2015). The optimized theta method. arXiv preprint arXiv:1503.03529.

Attributes
deseasonalize

Whether to deseasonalize the data

difference

Whether the data is differenced in the seasonality test

method

The method used to deseasonalize the data

period

The period of the seasonality

use_test

Whether to test the data for seasonality

Methods

 fit([use_mle, disp]) Estimate model parameters.

Methods

 fit([use_mle, disp]) Estimate model parameters.

Properties

 deseasonalize Whether to deseasonalize the data difference Whether the data is differenced in the seasonality test method The method used to deseasonalize the data period The period of the seasonality use_test Whether to test the data for seasonality