# tsCV

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Percentile

##### Time series cross-validation

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

Keywords
ts
##### 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 $y_1,\dots,y_T$. 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.

##### Value

Numerical time series object containing the forecast errors.

• tsCV
##### Examples
# NOT RUN {
#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)

# }

Documentation reproduced from package forecast, version 8.2, License: GPL-3

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