Type: Package
Title: Wavelet-ARIMA Model for Time Series Forecasting
Version: 0.1.2
Author: Dr. Ranjit Kumar Paul [aut, cre], Mr. Sandipan Samanta [aut], Dr. Md Yeasin [aut]
Maintainer: Dr. Ranjit Kumar Paul <ranjitstat@gmail.com>
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>.
License: GPL-3
Encoding: UTF-8
RoxygenNote: 7.2.0
Imports: stats, wavelets, fracdiff, forecast
NeedsCompilation: no
Packaged: 2022-07-02 13:49:29 UTC; YEASIN
Repository: CRAN
Date/Publication: 2022-07-02 21:50:08 UTC

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

References

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

References

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)