tsCV: Time series cross-validation

In forecast: Forecasting Functions for Time Series and Linear Models

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, window = NULL, xreg = NULL, initial = 0, ...)

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. If exogenous predictors are used, then it must also have xreg and newxreg arguments corresponding to the training and test periods.
h	Forecast horizon
window	Length of the rolling window, if NULL, a rolling window will not be used.
xreg	Exogeneous predictor variables passed to the forecast function if required.
initial	Initial period of the time series where no cross-validation is performed.
...	Other arguments are passed to forecastfunction.

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}. If h=1, these are returned as a vector, e_1,\dots,e_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. The time index corresponds to the last period of the training data. The columns correspond to the forecast horizons.

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)

#Example with exogenous predictors
far2_xreg <- function(x, h, xreg, newxreg) {
  forecast(Arima(x, order=c(2,0,0), xreg=xreg), xreg=newxreg)
}

y <- ts(rnorm(50))
xreg <- matrix(rnorm(100),ncol=2)
e <- tsCV(y, far2_xreg, h=3, xreg=xreg)

forecast documentation built on June 22, 2024, 9:20 a.m.