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WaveletArima  

Wavelet-ARIMA Model for Time Series Forecasting
View on CRAN: Click here


Download and install WaveletArima package within the R console
Install from CRAN:
install.packages("WaveletArima")

Install from Github:
library("remotes")
install_github("cran/WaveletArima")

Install by package version:
library("remotes")
install_version("WaveletArima", "0.1.2")



Attach the package and use:
library("WaveletArima")
Maintained by
Dr. Ranjit Kumar Paul
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2017-10-25
Latest Update: 2022-07-02
Description:
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>.
How to cite:
Dr. Ranjit Kumar Paul (2017). WaveletArima: Wavelet-ARIMA Model for Time Series Forecasting. R package version 0.1.2, https://cran.r-project.org/web/packages/WaveletArima. Accessed 21 Nov. 2024.
Previous versions and publish date:
0.1.0 (2017-10-25 10:57), 0.1.1 (2018-06-01 14:30)
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Complete documentation for WaveletArima
Functions, R codes and Examples using the WaveletArima R package
Some associated functions: WaveletFitting . WaveletFittingarma . 
Some associated R codes: WaveletFittingarma.R .  Full WaveletArima package functions and examples
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