Package WaveletArima
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1 Type Package Title Wavelet ARIMA Model Version Package WaveletArima Author Ranjit Kumar Paul and Sandipan Samanta June 1, 2018 Maintainer Ranjit Kumar Paul Description Fits hybrid Wavelet ARIMA model for time series forecasting using algorithm by Aminghafari and Poggi (2012) <doi: /s >. License GPL Imports stats, wavelets, fracdiff, forecast LazyData TRUE NeedsCompilation no Repository CRAN Date/Publication :30:25 UTC R topics documented: WaveletFitting WaveletFittingarma Index 5 WaveletFitting Wavelet transform using Maximal overlap discrete wavelet transform (MODWT) algorithm Description Transforms the time series data by using hybrid MODWT algorithm using haar filter. Usage WaveletFitting(ts,Wvlevels,bndry,FFlag) 1
2 2 WaveletFittingarma Arguments ts Wvlevels bndry FFlag univariate time series The level of wavelet decomposition The boundary condition of wavelet decomposition The FastFlag condition of wavelet decomposition: True or False Value WaveletFitting The wavelet trasnform of the series References Percival D. B. and Walden A. T Wavelet Methods for Time-Series Analysis. Cambridge Univ. Press, U.K. Paul R. K., Prajneshu and Ghosh H 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. Paul, R.K. and Birthal, P.S Investigating rainfall trend over India using wavelet technique. Journal of Water and Climate Change, 7, 365 to 378. Paul, R. K ARIMAX-GARCH-WAVELET Model for forecasting volatile data. Model Assisted Statistics and Application, 10, 243 to252. 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$series) Waveletlevels <- floor(log(length(simts))) # to obtain the maximum level for wavelet decomposition WS <- WaveletFitting(ts=simts,Wvlevels=Waveletlevels,bndry='periodic',FFlag=TRUE)$WaveletSeries WaveletFittingarma Wavelet-ARIMA hybrid model for forecasting Description Fits the time series data by using hybrid Wavelet-ARIMA algorithm.
3 WaveletFittingarma 3 Usage WaveletFittingarma(ts,Waveletlevels,boundary,FastFlag,MaxARParam,MaxMAParam,NForecast) Arguments ts Waveletlevels boundary FastFlag MaxARParam MaxMAParam NForecast univariate time series The level of wavelet decomposition The boundary condition of wavelet decomposition The FastFlag condition of wavelet decomposition: True or False The maximum AR order for auto.arima The maximum MA order for auto.arima The forecast horizon: A positive integer Value WaveletFittingarma The forecast of the series References Aminghafari, M. and Poggi, J.M Forecasting time series using wavelets. Internationa Journal of Wavelets, Multiresolution and Inforamtion Processing, 5, 709 to 724 Percival D. B. and Walden A. T Wavelet Methods for Time-Series Analysis. Cambridge Univ. Press, U.K. Paul R. K., Prajneshu and Ghosh H 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. Paul, R.K. and Birthal, P.S Investigating rainfall trend over India using wavelet technique. Journal of Water and Climate Change, 7, 365 to 378. Paul, R. K ARIMAX-GARCH-WAVELET Model for forecasting volatile data. Model Assisted Statistics and Application, 10, 243 to 252. 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)
4 4 WaveletFittingarma #Waveletlevels <- floor(log(length(simts))) # to obtain the maximum level for wavelet decomposition WaveletForecast<-WaveletFittingarma(ts=simts,Waveletlevels=floor(log(length(simts))), boundary='periodic',fastflag=true,maxarparam=5,maxmaparam=5,nforecast=5)
5 Index WaveletFitting, 1 WaveletFittingarma, 2 5
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