tsCV: Time series cross-validation

In a1967fa/f-cast: Forecasting Functions for Time Series and Linear Models

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, window = NULL, ...)

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
window	Length of the rolling window, if NULL, a rolling window will not be used.
...	Other arguments are passed to forecastfunction.

Details

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]. If h=1, these are returned as a vector, e[1:T]. For h>1, they are returned as a matrix with the hth column containing errors for forecast horizon h. The first few errors may be missing as it may not be possible to apply forecastfunction to very short time series.

Value

Numerical time series object containing the forecast errors as a vector (if h=1) and a matrix otherwise.

Author(s)

Rob J Hyndman

See Also

CV, CVar, residuals.Arima, https://robjhyndman.com/hyndsight/tscv/.

Examples

#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)

a1967fa/f-cast documentation built on May 29, 2019, 12:05 a.m.