WaveletFitting: Wavelet Transform Using Maximal Overlap Discrete Wavelet...

In WaveletArima: Wavelet-ARIMA Model for Time Series Forecasting

Wavelet Transform Using Maximal Overlap Discrete Wavelet Transform (MODWT) Algorithm

Description

Transforms the time series data by using hybrid MODWT algorithm.

Usage

WaveletFitting(
  ts,
  WFilter = "haar",
  Wvlevels,
  bndry = "periodic",
  FFlag = TRUE
)

Arguments

ts	Univariate time series
WFilter	Wavelet filter use in the decomposition
Wvlevels	The level of wavelet decomposition
bndry	The boundary condition of wavelet decomposition:'periodic' or 'reflection'
FFlag	The FastFlag condition of wavelet decomposition: True or False

Value

• WaveletSeries - The wavelet trasnform of the series

References

• 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.

Examples

data<-rnorm(100,mean=100,sd=50)
WaveletFitting(ts=data,Wvlevels=3,WFilter='haar',bndry='periodic',FFlag=TRUE)

WaveletArima documentation built on July 3, 2022, 1:05 a.m.