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WaveletArima: Wavelet-ARIMA Model for Time Series Forecasting
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>.
Getting started
Browse package contents
Package details |
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| Author | Dr. Ranjit Kumar Paul [aut, cre], Mr. Sandipan Samanta [aut], Dr. Md Yeasin [aut] |
| Maintainer | Dr. Ranjit Kumar Paul <ranjitstat@gmail.com> |
| License | GPL-3 |
| Version | 0.1.2 |
| Package repository | View on CRAN |
| Installation |
Install the latest version of this package by entering the following in R:
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