Package 'WaveletGBM'

Title: Wavelet Based Gradient Boosting Method
Description: Wavelet decomposition method is very useful for modelling noisy time series data. Wavelet decomposition using 'haar' algorithm has been implemented to developed hybrid Wavelet GBM (Gradient Boosting Method) model for time series forecasting using algorithm by Anjoy and Paul (2017) <DOI:10.1007/s00521-017-3289-9>.
Authors: Dr. Ranjit Kumar Paul [aut, cre], Dr. Md Yeasin [aut]
Maintainer: Dr. Ranjit Kumar Paul <[email protected]>
License: GPL-3
Version: 0.1.0
Built: 2025-01-27 03:55:53 UTC
Source: https://github.com/cran/WaveletGBM

Help Index

Wavelet Based Gradient Boosting Method

Description

Wavelet Based Gradient Boosting Method

Usage

WaveletGBM(ts, MLag = 12, split_ratio = 0.8, wlevels = 3)

Arguments

ts

Time Series Data
MLag

Maximum Lags
split_ratio

Training and Testing Split
wlevels

Number of Wavelet Levels

Value

  • Lag: Lags used in model

  • Parameters: Parameters of the model

  • 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

References

  • 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.

Examples

library("WaveletGBM")
data<- rnorm(100,100, 10)
WG<-WaveletGBM(ts=data)