WaveletFittingarma: Wavelet-ARIMA hybrid model for forecasting

In WaveletArima: Wavelet-ARIMA Model for Time Series Forecasting

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)

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