tsCV {forecast} | R Documentation |
tsCV
computes the forecast errors obtained by applying
forecastfunction
to subsets of the time series y
using a
rolling forecast origin.
tsCV(y, forecastfunction, h = 1, window = NULL, ...)
y |
Univariate time series |
forecastfunction |
Function to return an object of class
|
h |
Forecast horizon |
window |
Length of the rolling window, if NULL, a rolling window will not be used. |
... |
Other arguments are passed to |
Let y
contain the time series y[1:T]. Then
forecastfunction
is applied successively to the time series
y[1:t], for t=1,…,T-h, making predictions
f[t+h]. The errors are given by e[t+h] = y[t+h]-f[t+h]. These are returned as a
vector, e[1:T]. The first few errors may be missing as
it may not be possible to apply forecastfunction
to very short time
series.
Numerical time series object containing the forecast errors.
Rob J Hyndman
CV, CVar, residuals.Arima, https://robjhyndman.com/hyndsight/tscv/.
#Fit an AR(2) model to each rolling origin subset far2 <- function(x, h){forecast(Arima(x, order=c(2,0,0)), h=h)} e <- tsCV(lynx, far2, h=1) #Fit the same model with a rolling window of length 30 e <- tsCV(lynx, far2, h=1, window=30)