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.


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install.packages("TSPred")

5.1 by Rebecca Pontes Salles, 7 months ago


https://github.com/RebeccaSalles/TSPred/wiki


Report a bug at https://github.com/RebeccaSalles/TSPred/issues


Browse source code at https://github.com/cran/TSPred


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