rolling_window: Rolling forecasting origin for Multiresolution Forecasts
In Quirinms/MRFR: Multiresolution Forecasting

Description Usage Arguments Details Value Author(s) References Examples

This function computes a rolling forecasting origin for one- or multi-step forecasts with a specific method. Multi step forecasts are computed recursively with the one step forecast method.

1 2	rolling_window(UnivariateData, CoefficientCombination, Aggregation, Horizon = 2, Window = 3, Method = "r", NumClusters = 1)

`UnivariateData`	[1:n] Numerical vector with n values.
`CoefficientCombination`	[1:Scales+1] Numerical vector with numbers which are associated with wavelet levels. The last number is associated with the smooth level. Each number determines the number of coefficient used per level. The selection follows a specific scheme.
`Aggregation`	[1:Scales] Numerical vector carrying numbers whose index is associated with the wavelet level. The numbers indicate the number of time in points used for aggregation from the original time series.
`Horizon`	Number indicating horizon for forecast from 1 to horizon.
`Window`	Number indicating how many points are used for cross validation.
`Method`	String indicating which method to use. Available methods: 'r' = Autoregression. 'nn' = Neural Network.
`NumClusters`	Number of clusters used for parallel computing.

Thus, h-step forecast for h = 1,..., horizon for window_size many steps can be computed. The forecasting method can be an autoregression or a neural network (multilayer perceptron). The CoefficientCombination parameter controls the number of coefficients chosen for each wavelet and smooth part level individually. The NumClusters parameter determines the number of cluster used for parallel computation. NumClusters = 1 performs a non parallel version. NumClusters is constrained by the maximum number of clusters available minus one to prevent the machine to be overchallenged.

List of

`Error`	[1:Window,1:Horizon] Numerical Matrix with 'Window' many row entries indicating one time point with 'Horizon' many forecast errors.
`Forecast`	[1:Window,1:Horizon] Numerical Matrix with 'Window' many row 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(AirPassengers)
res = rolling_window(as.vector(array(AirPassengers)), c(10,10,10), c(2,4),
                           Horizon = 2, Window = 3, Method = "r",
                           NumClusters = 1)
Error = res$Error
Forecast = res$Forecast