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The main aim of this package is to combine the advantage of wavelet and support vector machine models for time series forecasting. This package also gives the accuracy measurements in terms of RMSE and MAPE. This package fits the hybrid Wavelet SVR model for time series forecasting The main aim of this package is to combine the advantage of wavelet and Support Vector Regression (SVR) models for time series forecasting. This package also gives the accuracy measurements in terms of Root Mean Square Error (RMSE) and Mean Absolute Prediction Error (MAPE). This package is based on the algorithm of Raimundo and Okamoto (2018) <doi:10.1109/INFOCT.2018.8356851>.
| Version: | 0.1.0 |
| Imports: | stats, wavelets, fracdiff, forecast, e1071, tsutils |
| Published: | 2022-01-06 |
| DOI: | 10.32614/CRAN.package.WaveletSVR |
| Author: | Ranjit Kumar Paul [aut, cre], Md Yeasin [aut] |
| Maintainer: | Ranjit Kumar Paul <ranjitstat at gmail.com> |
| License: | GPL-3 |
| NeedsCompilation: | no |
| CRAN checks: | WaveletSVR results |
| Reference manual: | WaveletSVR.pdf |
| Package source: | WaveletSVR_0.1.0.tar.gz |
| Windows binaries: | r-devel: WaveletSVR_0.1.0.zip, r-release: WaveletSVR_0.1.0.zip, r-oldrel: WaveletSVR_0.1.0.zip |
| macOS binaries: | r-release (arm64): WaveletSVR_0.1.0.tgz, r-oldrel (arm64): WaveletSVR_0.1.0.tgz, r-release (x86_64): WaveletSVR_0.1.0.tgz, r-oldrel (x86_64): WaveletSVR_0.1.0.tgz |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.