Time series cross-validation forecasting

Compute forecasts and other information by applying forecastfun to subsets of the time series y using a rolling forecast origin.

Usage

cvforecast(
  y,
  forecastfun,
  h = 1,
  level = c(80, 95),
  forward = TRUE,
  xreg = NULL,
  initial = 1,
  window = NULL,
  ...
)

Arguments

y

Univariate time series.

forecastfun

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 and an argument level for the confidence level for prediction intervals. If exogenous predictors are used, then it must also have xreg and newxreg arguments corresponding to the training and the test periods, respectively.

h

Forecast horizon. Defaults to 1.

level

Confidence level for prediction intervals, as a numeric vector of percentages. It must not be NULL, since the conformal methods need at least one interval to start from. Defaults to c(80, 95).

forward

If TRUE, the final forecast origin for forecasting is \(y_T\). Otherwise, the final forecast origin is \(y_{T-1}\). Defaults to TRUE.

xreg

Exogenous predictor variables passed to forecastfun if required. It should be of the same size as y+forward*h, otherwise, NA padding or subsetting will be applied. Defaults to NULL.

initial

Initial period of the time series where no cross-validation forecasting is performed. Defaults to 1.

window

Length of the rolling window. Defaults to NULL, in which case a rolling window will not be used.

...

Other arguments are passed to forecastfun.

Value

A list of class c("cvforecast", "forecast") with components:

x

The original time series.

series

The name of the series x.

xreg

Exogenous predictor variables used in the model, if applicable.

method

A character string "cvforecast".

fit_times

The number of times the model is fitted in cross-validation.

MEAN

Point forecasts as a multivariate time series, where the \(h\)th column holds the point forecasts for forecast horizon \(h\). The time index corresponds to the period for which the forecast is produced.

ERROR

Forecast errors given by \(e_{t+h|t} = y_{t+h}-\hat{y}_{t+h|t}\).

LOWER

A list containing lower bounds for prediction intervals for each level. Each element within the list will be a multivariate time series with the same dimensional characteristics as MEAN.

UPPER

A list containing upper bounds for prediction intervals for each level. Each element within the list will be a multivariate time series with the same dimensional characteristics as MEAN.

level

The confidence values associated with the prediction intervals.

call

The matched call.

forward

Whether forward is applied.

If forward is TRUE, the components mean, lower, upper, and model will also be returned, showing the information about the final fitted model and the forecasts using all available observations, see e.g. forecast.ets for more details.

Details

Let y denote the time series \(y_1,\dots,y_T\) and let \(t_0\) denote the initial period.

Suppose forward = TRUE. If window is NULL, forecastfun is applied successively to the subset time series \(y_{1},\dots,y_t\), for \(t=t_0,\dots,T\), generating forecasts \(\hat{y}_{t+1|t},\dots,\hat{y}_{t+h|t}\). If window is not NULL and has a length of \(t_w\), then forecastfun is applied successively to the subset time series \(y_{t-t_w+1},\dots,y_{t}\), for \(t=\max(t_0, t_w),\dots,T\).

If forward is FALSE, the last observation used for training will be \(y_{T-1}\).

See also

conformal and the conformal methods scp, acp, pid and acmcp, which all take the result of cvforecast() as their input; update.cpforecast to extend the results with new observations.

Examples

# Simulate time series from an AR(2) model
library(forecast)
set.seed(1)
series <- arima.sim(n = 200, list(ar = c(0.8, -0.5)), sd = sqrt(1))

# Example with a rolling window
far2 <- function(x, h, level) {
  Arima(x, order = c(2, 0, 0)) |>
    forecast(h = h, level)
}
fc <- cvforecast(series, forecastfun = far2, h = 3, level = 95, window = 50)
print(fc)
#> Cross-validation
#> 
#> Call:
#>  cvforecast(y = series, forecastfun = far2, h = 3, level = 95,  
#>      window = 50) 
#> 
#>  fit_times = 151 (the forward step included) 
#> 
#> Forecasts of the forward step:
#>     Point Forecast     Lo 95    Hi 95
#> 201      0.6271538 -1.412390 2.666698
#> 202      0.8607034 -1.661903 3.383310
#> 203      0.4935805 -2.031823 3.018984
summary(fc)
#> Cross-validation
#> 
#> Call:
#>  cvforecast(y = series, forecastfun = far2, h = 3, level = 95,  
#>      window = 50) 
#> 
#>  fit_times = 151 (the forward step included) 
#> 
#> Forecasts of the forward step:
#>     Point Forecast     Lo 95    Hi 95
#> 201      0.6271538 -1.412390 2.666698
#> 202      0.8607034 -1.661903 3.383310
#> 203      0.4935805 -2.031823 3.018984
#> 
#> Cross-validation error measures:
#>       ME   MAE   MSE RMSE    MPE    MAPE  MASE RMSSE Winkler_95 MSIS_95
#> CV 0.007 0.946 1.415 1.06 -3.933 269.763 0.992 0.882      5.875   6.194

# Example with exogenous predictors
far2_xreg <- function(x, h, level, xreg, newxreg) {
  Arima(x, order = c(2, 0, 0), xreg = xreg) |>
    forecast(h = h, level = level, xreg = newxreg)
}
fc_xreg <- cvforecast(series, forecastfun = far2_xreg, h = 3, level = 95,
                      xreg = matrix(rnorm(406), ncol = 2, nrow = 203),
                      window = 50)