WaveletFittingarma {WaveletArima}

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)