WaveletETS
Wavelet Based Error Trend Seasonality Model
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>.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.1.0 |
rolling linux/jammy R-4.5 | WaveletETS_0.1.0.tar.gz |
17.1 KiB |
0.1.0 |
rolling linux/noble R-4.5 | WaveletETS_0.1.0.tar.gz |
17.0 KiB |
0.1.0 |
rolling source/ R- | WaveletETS_0.1.0.tar.gz |
2.3 KiB |
0.1.0 |
latest linux/jammy R-4.5 | WaveletETS_0.1.0.tar.gz |
17.1 KiB |
0.1.0 |
latest linux/noble R-4.5 | WaveletETS_0.1.0.tar.gz |
17.0 KiB |
0.1.0 |
latest source/ R- | WaveletETS_0.1.0.tar.gz |
2.3 KiB |
0.1.0 |
2026-04-26 source/ R- | WaveletETS_0.1.0.tar.gz |
2.3 KiB |
0.1.0 |
2026-04-23 source/ R- | WaveletETS_0.1.0.tar.gz |
2.3 KiB |
0.1.0 |
2026-04-09 windows/windows R-4.5 | WaveletETS_0.1.0.zip |
20.0 KiB |
0.1.0 |
2025-04-20 source/ R- | WaveletETS_0.1.0.tar.gz |
2.3 KiB |