WaveletGBM {WaveletGBM}

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)