`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, ...)

## 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` . |

## Value

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

## 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.

## See also

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

## Examples