model_selection: Model Selection for Multiresolution Forecasts
In Quirinms/MRFR: Multiresolution Forecasting

Description Usage Arguments Details Value Author(s) References Examples

This function computes a model selection using a criterion (AIC, MRE).

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model_selection(UnivariateData, Aggregation, Horizon = 14, Window = 365,
Method = "r", crit = "AIC", itermax = 1, lower_limit = 1, upper_limit = 2,
NumClusters = 1)

`UnivariateData`	[1:n] Numerical vector with n values.
`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.
`crit`	String indicating which criterion to use. Available criterion: AIC = Akaikes Information Criterion. MRE = Mean Root Error.
`itermax`	Number of iterations for evolutionary optimization method.
`lower_limit`	Lower limit for coefficients selected for each level
`upper_limit`	Higher limit for coefficients selected for each level
`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).

`Error`	[1:Window, 1:Horizon] Numerical Matrix with 'Window' many rows entries indicating one time point with 'Horizon' many forecast errors.
`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.

Quirin Stier

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

data(entsoe)
model_selection(UnivariateData = entsoe$value, Aggregation = c(2,4), Horizon = 1,
Window = 1, Method = "r", crit = "AIC", itermax = 1, upper_limit = 1,
NumClusters = 1)