| Type: | Package |
| Title: | Wavelet Based Gradient Boosting Method |
| Version: | 0.1.0 |
| Author: | Dr. Ranjit Kumar Paul [aut, cre], Dr. Md Yeasin [aut] |
| Maintainer: | Dr. Ranjit Kumar Paul <ranjitstat@gmail.com> |
| 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>. |
| License: | GPL-3 |
| Encoding: | UTF-8 |
| Imports: | caret, dplyr, caretForecast, Metrics, tseries, stats, wavelets, gbm |
| RoxygenNote: | 7.2.1 |
| NeedsCompilation: | no |
| Packaged: | 2023-04-06 07:54:16 UTC; YEASIN |
| Repository: | CRAN |
| Date/Publication: | 2023-04-07 08:20:02 UTC |
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)