| Title: | Wavelet Based Error Trend Seasonality Model |
|---|---|
| Description: | ETS stands for Error, Trend, and Seasonality, and it is a popular time series forecasting method. Wavelet decomposition can be used for denoising, compression, and feature extraction of signals. By removing the high-frequency components, wavelet decomposition can remove noise from the data while preserving important features. A hybrid Wavelet ETS (Error Trend-Seasonality) model has been developed for time series forecasting using algorithm of Anjoy and Paul (2017) <DOI:10.1007/s00521-017-3289-9>. |
| Authors: | Dr. Ranjit Kumar Paul [aut], Dr. Md Yeasin [aut, cre] |
| Maintainer: | Dr. Md Yeasin <[email protected]> |
| License: | GPL-3 |
| Version: | 0.1.0 |
| Built: | 2025-02-17 03:29:37 UTC |
| Source: | https://github.com/cran/WaveletETS |
Wavelet Based Error Trend Seasonality Model
WaveletETS(ts, split_ratio = 0.8, wlevels = 3)WaveletETS(ts, split_ratio = 0.8, wlevels = 3)
ts |
Time Series Data |
split_ratio |
Training and Testing Split |
wlevels |
Number of Wavelet Levels |
Train_actual: Actual train series
Test_actual: Actual test series
Train_fitted: Fitted train series
Test_predicted: Predicted test series
Accuracy: RMSE and MAPE of the model
Aminghafari, M. and Poggi, J.M. 2012. Nonstationary time series forecasting using wavelets and kernel smoothing. Communications in Statistics-Theory and Methods, 41(3),485-499.
Paul, R.K. A and Anjoy, P. 2018. Modeling fractionally integrated maximum temperature series in India in presence of structural break. Theory and Applied Climatology 134, 241–249.
library("WaveletETS") data<- rnorm(100,100, 10) WG<-WaveletETS(ts=data)library("WaveletETS") data<- rnorm(100,100, 10) WG<-WaveletETS(ts=data)