forecast (version 8.1)

tsCV: Time series cross-validation

Description

tsCV computes the forecast errors obtained by applying forecastfunction to subsets of the time series y using a rolling forecast origin.

Usage

tsCV(y, forecastfunction, h = 1, ...)

Arguments

y

Univariate time series

forecastfunction

Function to return an object of class forecast. Its first argument must be a univariate time series, and it must have an argument h for the forecast horizon.

h

Forecast horizon

...

Other arguments are passed to forecastfunction.

Value

Numerical time series object containing the forecast errors.

Details

Let y contain the time series \(y_1,\dots,y_T\). Then forecastfunction is applied successively to the time series \(y_1,\dots,y_t\), for \(t=1,\dots,T-h\), making predictions \(\hat{y}_{t+h|t}\). The errors are given by \(e_{t+h} = y_{t+h}-\hat{y}_{t+h|t}\). These are returned as a vector, \(e_1,\dots,e_T\). The first few errors may be missing as it may not be possible to apply forecastfunction to very short time series.

See Also

CV, CVar, residuals.Arima.

Examples

# NOT RUN {
#Fit an AR(2) model to each subset
far2 <- function(x, h){forecast(Arima(x, order=c(2,0,0)), h=h)}
e <- tsCV(lynx, far2, h=1)

# }