CVar: k-fold Cross-Validation applied to an autoregressive model

In pli2016/forecast: Forecasting Functions for Time Series and Linear Models

Description

CVar computes the errors obtained by applying an autoregressive modelling function to subsets of the time series y using k-fold cross-validation as described in Bergmeir, Hyndman and Koo (2015).

Usage

CVar(y, k=10, FUN=nnetar, cvtrace=FALSE, ...)

Arguments

y	Univariate time series
k	Number of folds to use for cross-validation.
FUN	Function to fit an autoregressive model. Currently, it only works with the nnetar function.
cvtrace	Provide progress information.
...	Other arguments are passed to FUN.

Value

A list containing information about the model and accuracy for each fold, plus other summary information computed across folds.

Author(s)

Gabriel Caceres and Rob J Hyndman

References

Bergmeir, C., Hyndman, R.J., Koo, B. (2015) A note on the validity of cross-validation for evaluating time series prediction. Monash working paper 10/15. http://robjhyndman.com/working-papers/cv-time-series/.

See Also

CV, tsCV.

Examples

modelcv <- CVar(lynx, k=5, lambda=0.15)
print(modelcv)
print(modelcv$fold1)

pli2016/forecast documentation built on May 25, 2019, 8:22 a.m.