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WaveletRF

Wavelet-RF Hybrid Model for Time Series Forecasting

v0.1.0 · Feb 21, 2022 · GPL-3

Description

The Wavelet Decomposition followed by Random Forest Regression (RF) models have been applied for time series forecasting. The maximum overlap discrete wavelet transform (MODWT) algorithm was chosen as it works for any length of the series. The series is first divided into training and testing sets. In each of the wavelet decomposed series, the supervised machine learning approach namely random forest was employed to train the model. This package also provides accuracy metrics in the form of Root Mean Square Error (RMSE) and Mean Absolute Prediction Error (MAPE). This package is based on the algorithm of Ding et al. (2021) <DOI: 10.1007/s11356-020-12298-3>.

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OK 13 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE Jun 9, 2026
ERROR 12 OK · 0 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE Jun 8, 2026
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OK 14 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE Mar 10, 2026

Dependency Network

Dependencies Reverse dependencies wavelets fracdiff forecast randomForest tsutils WaveletRF

Version History

1 tracked
new 0.1.0 Mar 10, 2026

R Observatory began tracking this package on Mar 10, 2026; it first appeared on CRAN Feb 22, 2022. Releases before tracking aren’t shown.