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Wavelet decomposes a series into multiple sub series called detailed and smooth components which helps to capture volatility at multi resolution level by various models. Two hybrid Machine Learning (ML) models (Artificial Neural Network and Support Vector Regression have been used) have been developed in combination with stochastic models, feature selection, and optimization algorithms for prediction of the data. The algorithms have been developed following Paul and Garai (2021) <doi:10.1007/s00500-021-06087-4>.
| Version: | 0.1.0 |
| Imports: | stats, utils, wavelets, tseries, forecast, fGarch, aTSA, FinTS, LSTS, earth, caret, neuralnet, e1071, pso |
| Published: | 2023-04-05 |
| DOI: | 10.32614/CRAN.package.WaveletML |
| Author: | Mr. Sandip Garai [aut, cre], Dr. Ranjit Kumar Paul [aut], Dr. Md Yeasin [aut] |
| Maintainer: | Mr. Sandip Garai <sandipnicksandy at gmail.com> |
| License: | GPL-3 |
| NeedsCompilation: | no |
| CRAN checks: | WaveletML results |
| Reference manual: | WaveletML.pdf |
| Package source: | WaveletML_0.1.0.tar.gz |
| Windows binaries: | r-devel: WaveletML_0.1.0.zip, r-release: WaveletML_0.1.0.zip, r-oldrel: WaveletML_0.1.0.zip |
| macOS binaries: | r-release (arm64): WaveletML_0.1.0.tgz, r-oldrel (arm64): WaveletML_0.1.0.tgz, r-release (x86_64): WaveletML_0.1.0.tgz, r-oldrel (x86_64): WaveletML_0.1.0.tgz |
| Reverse imports: | WaveletMLbestFL |
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These binaries (installable software) and packages are in development.
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