theta_model: Theta model

In forecast: Forecasting Functions for Time Series and Linear Models

Theta model

Description

The theta method of Assimakopoulos and Nikolopoulos (2000) is equivalent to simple exponential smoothing with drift (Hyndman and Billah, 2003). This function fits the theta model to a time series. 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 fitting the theta model.

Usage

theta_model(y, lambda = NULL, biasadj = FALSE)

Arguments

y	a numeric vector or univariate time series of class ts
lambda	Box-Cox transformation parameter. If lambda = "auto", then a transformation is automatically selected using BoxCox.lambda. The transformation is ignored if NULL. Otherwise, data transformed before model is estimated.
biasadj	Use adjusted back-transformed mean for Box-Cox transformations. If transformed data is used to produce forecasts and fitted values, a regular back transformation will result in median forecasts. If biasadj is TRUE, an adjustment will be made to produce mean forecasts and fitted values.

Details

More general theta methods are available in the forecTheta package.

Value

An object of class theta_model.

Author(s)

Rob J Hyndman

References

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

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

See Also

thetaf()

Examples

nile_fit <- theta_model(Nile)
forecast(nile_fit) |> autoplot()

forecast documentation built on March 18, 2026, 9:07 a.m.