| Title: | Wavelet ANN Model |
|---|---|
| Description: | The wavelet and ANN technique have been combined to reduce the effect of data noise. This wavelet-ANN conjunction model is able to forecast time series data with better accuracy than the traditional time series model. This package fits hybrid Wavelet ANN 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: | 0.1.2 |
| Built: | 2025-01-31 03:23:38 UTC |
| Source: | https://github.com/cran/WaveletANN |
Wavelet Transform Using Maximal Overlap Discrete Wavelet Transform (MODWT) Algorithm
WaveletFitting(ts, Wvlevels, Filter = "haar", bndry = "periodic", FFlag = TRUE)WaveletFitting(ts, Wvlevels, Filter = "haar", bndry = "periodic", FFlag = TRUE)
ts |
Univariate time series |
Wvlevels |
The level of wavelet decomposition |
Filter |
Wavelet filter |
bndry |
The boundary condition of wavelet decomposition |
FFlag |
The FastFlag condition of wavelet decomposition: True or False |
WaveletSeries - The wavelet transform of the series
Aminghafari, M. and Poggi, J.M. 2007. Forecasting time series using wavelets. Internationa Journal of Wavelets, Multiresolution and Inforamtion Processing, 5, 709 to 724
Percival D. B. and Walden A. T. 2000. Wavelet Methods for Time-Series Analysis. Cambridge Univ. Press, U.K.
Paul R. K., Prajneshu and Ghosh H. 2013. Wavelet Frequency Domain Approach for Modelling and Forecasting of Indian Monsoon Rainfall Time-Series Data. Journal of the Indian society of agricultural statistics, 67, 319 to 327.
data<-rnorm(100,mean=100,sd=50) WaveletFitting(ts=data,Wvlevels=3,Filter='haar',bndry='periodic',FFlag=TRUE)data<-rnorm(100,mean=100,sd=50) WaveletFitting(ts=data,Wvlevels=3,Filter='haar',bndry='periodic',FFlag=TRUE)
Wavelet-ANN Hybrid Model for Forecasting
WaveletFittingann( ts, Waveletlevels, Filter = "haar", boundary = "periodic", FastFlag = TRUE, nonseaslag, seaslag = 1, hidden, NForecast )WaveletFittingann( ts, Waveletlevels, Filter = "haar", boundary = "periodic", FastFlag = TRUE, nonseaslag, seaslag = 1, hidden, NForecast )
ts |
Univariate time series |
Waveletlevels |
The level of wavelet decomposition |
Filter |
Wavelet filter |
boundary |
The boundary condition of wavelet decomposition |
FastFlag |
The FastFlag condition of wavelet decomposition: True or False |
nonseaslag |
Number of non seasonal lag |
seaslag |
Number of non seasonal lag |
|
Size of the hidden layer |
|
NForecast |
The forecast horizon: A positive integer |
Finalforecast - Forecasted value
FinalPrediction - Predicted value of train data
Accuracy - RMSE and MAPE for train data
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.
N <- 100 PHI <- 0.2 THETA <- 0.1 SD <- 1 M <- 0 D <- 0.2 Seed <- 123 set.seed(Seed) Sim.Series <- fracdiff::fracdiff.sim(n = N,ar=c(PHI),ma=c(THETA),d=D,rand.gen =rnorm,sd=SD,mu=M) simts <- as.ts(Sim.Series$series) WaveletForecast<-WaveletFittingann(ts=simts,Waveletlevels=3,Filter='d4', nonseaslag=5,hidden=3,NForecast=5)N <- 100 PHI <- 0.2 THETA <- 0.1 SD <- 1 M <- 0 D <- 0.2 Seed <- 123 set.seed(Seed) Sim.Series <- fracdiff::fracdiff.sim(n = N,ar=c(PHI),ma=c(THETA),d=D,rand.gen =rnorm,sd=SD,mu=M) simts <- as.ts(Sim.Series$series) WaveletForecast<-WaveletFittingann(ts=simts,Waveletlevels=3,Filter='d4', nonseaslag=5,hidden=3,NForecast=5)