TSPred: 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.
| Version: |
5.1 |
| Depends: |
R (≥ 3.5.0) |
| Imports: |
forecast, KFAS, stats, MuMIn, EMD, wavelets, vars, ModelMetrics, RSNNS, Rlibeemd, e1071, elmNNRcpp, nnet, randomForest, magrittr, plyr, methods, dplyr, keras, tfdatasets |
| Published: |
2021-01-21 |
| DOI: |
10.32614/CRAN.package.TSPred |
| Author: |
Rebecca Pontes Salles [aut, cre, cph] (CEFET/RJ),
Eduardo Ogasawara [ths] (CEFET/RJ) |
| Maintainer: |
Rebecca Pontes Salles <rebeccapsalles at acm.org> |
| BugReports: |
https://github.com/RebeccaSalles/TSPred/issues |
| License: |
GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: |
https://github.com/RebeccaSalles/TSPred/wiki |
| NeedsCompilation: |
no |
| Citation: |
TSPred citation info |
| CRAN checks: |
TSPred results |
Documentation:
Downloads:
Reverse dependencies:
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