Package TSF. July 15, 2017
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1 Type Package Package TSF July 15, 2017 Title Two Stage Forecasting (TSF) for Long Memory Time Series in Presence of Structural Break Version Author Sandipan Samanta, Ranjit Kumar Paul and Dipankar Mitra Maintainer Dr. Ranjit Kumar Paul Description Forecasting of long memory time series in presence of structural break by using TSF algorithm by Papailias and Dias (2015) <doi: /j.ijforecast >. License GPL Imports stats, fracdiff, forecast LazyData TRUE NeedsCompilation no Repository CRAN Date/Publication :49:07 UTC R topics documented: forecasttsf StructuralBrekwithLongmemory TSF Index 6 forecasttsf Forecasting fractionally differenced series using TSF approach Description The function is used for forecasting long memory time series using TSF approach 1
2 2 forecasttsf Usage forecasttsf(n0,xt,bandwidth) Arguments N0 Xt bandwidth lead period of forecast univariate time series the bandwidth used in the regression equation Value forecasttsf the predicted values, the out of sample forecasts and the values of long memory parameter Author(s) Sandipan Samanta, Ranjit Kumar Paul and Dipankar Mitra References Papailias, F. and Dias, G. F Forecasting long memory series subject to structural change: A two-stage approach. International Journal of Forecasting, 31, 1056 to Wang, C. S. H., Bauwens, L. and Hsiao, C Forecasting a long memory process subject to structural breaks. Journal of Econometrics, 177, Reisen, V. A. (1994) Estimation of the fractional difference parameter in the ARFIMA(p,d,q) model using the smoothed periodogram. Journal Time Series Analysis, 15(1), 335 to 350. Examples ## Simulating Long Memory Series N < PHI <- 0.2 THETA <- 0.1 SD <- 1 M <- 0 D <- 0.2 Seed <- 123 N0<-9 bandwidth<-0.9 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) Xt <- as.ts(sim.series$series) ## Forecasting using TSF method forecasttsf (N0,Xt,bandwidth)
3 StructuralBrekwithLongmemory 3 StructuralBrekwithLongmemory Predicting fractionally differenced series in presence of structural break Description The function is used for prediction of long memory time series in presence of structural break Usage StructuralBrekwithLongmemory(ts,bandwidth) Arguments ts bandwidth univariate time series the bandwidth used in the regression equation Value StructuralBrekwithLongmemory the updated series at first step of TSF appraoch, prediction based on TSF approach and the estimate of long memory parameter Author(s) Sandipan Samanta, Ranjit Kumar Paul and Dipankar Mitra References Papailias, F. and Dias, G. F Forecasting long memory series subject to structural change: A two-stage approach. International Journal of Forecasting, 31, 1056 to Wang, C. S. H., Bauwens, L. and Hsiao, C Forecasting a long memory process subject to structural breaks. Journal of Econometrics, 177, Reisen, V. A. (1994) Estimation of the fractional difference parameter in the ARFIMA(p,d,q) model using the smoothed periodogram. Journal Time Series Analysis, 15(1), 335 to 350. Examples ## Simulating Long Memory Series N < PHI <- 0.2 THETA <- 0.1 SD <- 1 M <- 0 D <- 0.2 Seed <- 123 bandwidth<-0.9
4 4 TSF set.seed(seed) Sim.Series <- fracdiff::fracdiff.sim(n = N, ar = c(phi), ma = c(theta), d = D, rand.gen = rnorm, sd = SD, mu = M) Xt <- as.ts(sim.series$series) ## Forecasting using TSF method StructuralBrekwithLongmemory(Xt,bandwidth) TSF Fractionally differenced series for any value of d Description The function fdseries computes the fractional differenced series for any value of d i.e. positive or negetive. Usage fdseries(x, d) Arguments x d univariate time series The orer of fractional differencing to be done Value fdseries fractionally differenced series for both positive as well as negetive d Author(s) Sandipan Samanta, Ranjit Kumar Paul and Dipankar Mitra References Papailias, F. and Dias, G. F Forecasting long memory series subject to structural change: A two-stage approach. International Journal of Forecasting, 31, 1056 to Wang, C. S. H., Bauwens, L. and Hsiao, C Forecasting a long memory process subject to structural breaks. Journal of Econometrics, 177, Reisen, V. A. (1994) Estimation of the fractional difference parameter in the ARFIMA(p,d,q) model using the smoothed periodogram. Journal Time Series Analysis, 15(1), 335 to 350.
5 TSF 5 Examples ## Simulating Long Memory Series N < 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) Xt <- as.ts(sim.series$series) ## fractional differencing fdseries(xt,d=d)
6 Index fdseries (TSF), 4 forecasttsf, 1 StructuralBrekwithLongmemory, 3 TSF, 4 6
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