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Noise in the time-series data significantly affects the accuracy of the ARIMA model. Wavelet transformation decomposes the time series data into subcomponents to reduce the noise and help to improve the model performance. The wavelet-ARIMA model can achieve higher prediction accuracy than the traditional ARIMA model. This package provides Wavelet-ARIMA model for time series forecasting based on the algorithm by Aminghafari and Poggi (2012) and Paul and Anjoy (2018) <doi:10.1142/S0219691307002002> <doi:10.1007/s00704-017-2271-x>.
| Version: | 0.1.2 |
| Imports: | stats, wavelets, fracdiff, forecast |
| Published: | 2022-07-02 |
| Author: | Dr. Ranjit Kumar Paul [aut, cre], Mr. Sandipan Samanta [aut], Dr. Md Yeasin [aut] |
| Maintainer: | Dr. Ranjit Kumar Paul <ranjitstat at gmail.com> |
| License: | GPL-3 |
| NeedsCompilation: | no |
| CRAN checks: | WaveletArima results |
| Reference manual: | WaveletArima.pdf |
| Package source: | WaveletArima_0.1.2.tar.gz |
| Windows binaries: | r-devel: WaveletArima_0.1.2.zip, r-release: WaveletArima_0.1.2.zip, r-oldrel: WaveletArima_0.1.2.zip |
| macOS binaries: | r-release (arm64): WaveletArima_0.1.2.tgz, r-oldrel (arm64): WaveletArima_0.1.2.tgz, r-release (x86_64): WaveletArima_0.1.2.tgz, r-oldrel (x86_64): WaveletArima_0.1.2.tgz |
| Old sources: | WaveletArima archive |
| Reverse imports: | hybridts |
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