Package 'WaveletArima'

Title: Wavelet-ARIMA Model for Time Series Forecasting
Description: Noise in the time-series data significantly affects the accuracy of the ARIMA model. Wavelet transformation decomposes the time series data into subcomponents to reduce the noise and help to improve the model performance. The wavelet-ARIMA model can achieve higher prediction accuracy than the traditional ARIMA model. This package provides Wavelet-ARIMA model for time series forecasting based on the algorithm by Aminghafari and Poggi (2012) and Paul and Anjoy (2018) <doi:10.1142/S0219691307002002> <doi:10.1007/s00704-017-2271-x>.
Authors: Dr. Ranjit Kumar Paul [aut, cre], Mr. Sandipan Samanta [aut], Dr. Md Yeasin [aut]
Maintainer: Dr. Ranjit Kumar Paul <[email protected]>
License: GPL-3
Version: 0.1.2
Built: 2025-02-10 03:28:58 UTC
Source: https://github.com/cran/WaveletArima

Help Index

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)

Wavelet-ARIMA hybrid model for forecasting

Description

Fits the time series data by using hybrid Wavelet-ARIMA algorithm.

Usage

WaveletFittingarma(
  ts,
  filter = "haar",
  Waveletlevels,
  boundary = "periodic",
  FastFlag = TRUE,
  MaxARParam,
  MaxMAParam,
  NForecast
)

Arguments

ts

univariate time series
filter

Wavelet filter use in the decomposition
Waveletlevels

The level of wavelet decomposition
boundary

The boundary condition of wavelet decomposition
FastFlag

The FastFlag condition of wavelet decomposition: True or False
MaxARParam

The maximum AR order for auto.arima
MaxMAParam

The maximum MA order for auto.arima
NForecast

The forecast horizon: A positive integer

Value

  • Finalforecast - Forecasted value

  • FinalPrediction - Predicted value of train data

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

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<-WaveletFittingarma(ts=simts,filter ='la8',Waveletlevels=floor(log(length(simts))),
MaxARParam=5,MaxMAParam=5,NForecast=5)