The following function produces a simple MAPA forecast from a given origin. This is meant to be an internal function. Use mapafor instead.
mapacalc(y, mapafit, fh = 0, comb = c("w.mean","w.median","mean","median","wght"),
outplot = c(0,1,2), hybrid = c(TRUE,FALSE), xreg=NULL)Vector with forecasts.
Array with MAPA components.
In sample observations of a time series (vector).
Fitted MAPA model (from mapaest).
Forecast horizon. Default = ppy.
Combination operator. This can be: "mean"; "median"; "wght" - where each aggregation level is weighted inversly to aggregation; "w.mean" - level and trend components are averaged, but seasonal and xreg follow the wght combination; "w.median" - as w.mean, but with median. It is suggested that for data with high sampling frequency to use one of the "w.mean" and "w.median".
Provide output plot. 0 = no; 1 = time series and forecast only; 2 = time series, forecasts and components. For the components the spectral colouring scheme is used. Dark red is aggregation level 1. Default is 1.
Provide hybrid forecasts, as in Kourentzes et al. paper. If minimumAL > 1 then the minimumAL ETS forecasts are used. Default is TRUE.
Vector or matrix of exogenous variables to be included in the MAPA. If matrix then rows are observations and columns are variables. Must be at least as long as in-sample plus fh. Additional observations are unused.
Nikolaos Kourentzes, nikolaos@kourentzes.com; Fotios Petropoulos.
Kourentzes N., Petropoulos F., Trapero J.R. (2014) Improving forecasting by estimating time series structural components across multiple frequencies. International Journal of Forecasting, 30(2), 291--302.
Kourentzes N., Petropoulos F. (2015) Forecasting with multivariate temporal aggregation: The case of promotional modelling. International Journal of Production Economics.
You can find more information about MAPA at Nikos' blog.
mapafor, mapa.
mapafit <- mapaest(admissions,outplot=0)
mapacalc(admissions,mapafit,outplot=2)
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