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>.

Contains functions for computing and plotting
        discrete wavelet transforms (DWT) and maximal overlap discrete
        wavelet transforms (MODWT), as well as their inverses.
        Additionally, it contains functionality for computing and
        plotting wavelet transform filters that are used in the above
        decompositions as well as multiresolution analyses.

The main aim of this package is to combine the advantage of wavelet and support vector machine models for time series forecasting. This package also gives the accuracy measurements in terms of RMSE and MAPE. This package fits the hybrid Wavelet SVR model for time series forecasting The main aim of this package is to combine the advantage of wavelet and Support Vector Regression (SVR) models for time series forecasting. This package also gives the accuracy measurements in terms of Root Mean Square Error (RMSE) and Mean Absolute Prediction Error (MAPE). This package is based on the algorithm of Raimundo and Okamoto (2018) <DOI: 10.1109/INFOCT.2018.8356851>.

Wavelet routines that calculate single sets of wavelet
    multiple regressions and correlations, and cross-regressions and
    cross-correlations from a multivariate time series.  Dynamic versions
    of the routines allow the wavelet local multiple (cross-)regressions
    and (cross-)correlations to evolve over time.