WaveletANN (version 0.1.2)

WaveletFittingann: Wavelet-ANN Hybrid Model for Forecasting

Description

Wavelet-ANN Hybrid Model for Forecasting

Usage

WaveletFittingann(
  ts,
  Waveletlevels,
  Filter = "haar",
  boundary = "periodic",
  FastFlag = TRUE,
  nonseaslag,
  seaslag = 1,
  hidden,
  NForecast
)

Value

• Finalforecast - Forecasted value

• FinalPrediction - Predicted value of train data

• Accuracy - RMSE and MAPE for train data

Arguments

ts

Univariate time series

Waveletlevels

The level of wavelet decomposition

Filter

Wavelet filter

boundary

The boundary condition of wavelet decomposition

FastFlag

The FastFlag condition of wavelet decomposition: True or False

nonseaslag

Number of non seasonal lag

seaslag

Number of non seasonal lag

hidden

Size of the hidden layer

NForecast

The forecast horizon: A positive integer

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<-WaveletFittingann(ts=simts,Waveletlevels=3,Filter='d4',
nonseaslag=5,hidden=3,NForecast=5)