Functions for Benchmarking Time Series Prediction

Functions for defining and conducting a time series prediction process including pre(post)processing, decomposition, modelling, prediction and accuracy assessment. The generated models and its yielded prediction errors can be used for benchmarking other time series prediction methods and for creating a demand for the refinement of such methods. For this purpose, benchmark data from prediction competitions may be used.


Reference manual

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5.1 by Rebecca Pontes Salles, a year ago

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Authors: Rebecca Pontes Salles [aut, cre, cph] (CEFET/RJ) , Eduardo Ogasawara [ths] (CEFET/RJ)

Documentation:   PDF Manual  

GPL (>= 2) license

Imports forecast, KFAS, stats, MuMIn, EMD, wavelets, vars, ModelMetrics, RSNNS, Rlibeemd, e1071, elmNNRcpp, nnet, randomForest, magrittr, plyr, methods, dplyr, keras, tfdatasets

Imported by predtoolsTS.

See at CRAN