THETA | R Documentation |
The theta method of Assimakopoulos and Nikolopoulos (2000) is equivalent to simple exponential smoothing with drift. This is demonstrated in Hyndman and Billah (2003).
THETA(formula, ...)
formula |
Model specification. |
... |
Not used. |
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 reseasonalized.
More general theta methods are available in the forecTheta package.
A model specification.
The season
special is used to specify the parameters of the seasonal adjustment via classical decomposition.
season(period = NULL, method = c("multiplicative", "additive"))
period | The periodic nature of the seasonality. This can be either a number indicating the number of observations in each seasonal period, or text to indicate the duration of the seasonal window (for example, annual seasonality would be "1 year"). |
method | The type of classical decomposition to apply. The original Theta method always used multiplicative seasonal decomposition, and so this is the default. |
Rob J Hyndman, Mitchell O'Hara-Wild
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.
# Theta method with transform
deaths <- as_tsibble(USAccDeaths)
deaths %>%
model(theta = THETA(log(value))) %>%
forecast(h = "4 years") %>%
autoplot(deaths)
# Compare seasonal specifications
library(tsibbledata)
library(dplyr)
aus_retail %>%
filter(Industry == "Clothing retailing") %>%
model(theta_multiplicative = THETA(Turnover ~ season(method = "multiplicative")),
theta_additive = THETA(Turnover ~ season(method = "additive"))) %>%
accuracy()
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