nested_cross_validation: Nested cross validation for Multiresolution Forecasts
In Quirinms/MRFR: Multiresolution Forecasting

Description Usage Arguments Details Value Author(s) References Examples

View source: R/nested_cross_validation.R

This function computes a nested cross validation (with the rolling forecasting origin). The data is split into 3 datasets: training, test and evaluation dataset. The best model is selected on the test and its performance is measured on the evaluation dataset.

1 2	nested_cross_validation(UnivariateData, Horizon=14, EvaluationLength=2, TestLength=2, Method = "r", MultivariateData=NULL, NumMV=1, NumClusters = 1)

`UnivariateData`	[1:n] Numerical vector with n values.
`Horizon`	Number indicating horizon for forecast from 1 to horizon.
`EvaluationLength`	Number indicating how many points are used for cross validation for the evaluation dataset.
`TestLength`	Number indicating how many points are used for cross validation for the test dataset.
`Method`	String indicating which method to use. Available methods: 'r' = Autoregression. 'nn' = Neural Network.
`MultivariateData`	Not implemented yet.
`NumMV`	Not implemented yet.
`NumClusters`	Number of clusters used for parallel computing.

The evaluation function (optimization function) is built with a rolling forecasting origin (rolling_window function), which computes a h-step ahead forecast (for h = 1, ..., horizon) for window_size many steps. The input space is searched with an evolutionary optimization method. The deployed forecast method can be an autoregression or a neural network (multilayer perceptron with one hidden layer).

`Best`	[1:Scales+1] Numerical vector with integers associated with the best found number of coefficients per wavelet scale (1:Scales) and number of coefficients for the smooth approximation level in the last entry.
`Error`	[1:Window, 1:Horizon] Numerical Matrix with 'Window' many rows entries indicating one time point with 'Horizon' many forecast errors.
`Forecast`	[1:Window, 1:Horizon] Numerical Matrix with 'Window' many rows entries indicating one time point with 'Horizon' many forecasts.

Quirin Stier

Hyndman, R. and Athanasopoulos, G. Forecasting: principles and practice. OTexts, 3 edition. 2018.

data(entsoe)
res = nested_cross_validation(entsoe$value, Horizon = 2, EvaluationLength=2,
TestLength=2, Method="r", MultivariateData=NULL, NumMV=1, NumClusters=1)
BestCoefficientCombination = res$Best
Error = res$Error
Forecast = res$Forecast