ftsa (version 1.2)

dynupdate: Dynamic updates via BM, OLS, RR and PLS methods

Description

Four methods, namely block moving (BM), ordinary least squares (OLS) regression, ridge regression (RR), penalized least squares (PLS) regression, were proposed to address the problem of dynamic updating, when partial data in the most recent curve are observed.

Usage

dynupdate(data, newdata = NULL, holdoutdata, method = c("ts", "block", 
 "ols", "pls", "ridge"), fmethod = c("arima", "ar", "ets", "ets.na", 
  "rwdrift", "rw"), error = c("mse", "mae", "mape"), order = 6,
   lambda = 0.01, value = FALSE, interval = FALSE, 
    level = 80, B = 1000)

Value

forecastsAn object of class fts containing the dynamic updated point forecasts.bootsampAn object of class fts containing the bootstrapped point forecasts, which are updated by the PLS method.lowAn object of class fts containing the lower bound of prediction intervals.upAn object of class fts containing the upper bound of prediction intervals.

Details

This function is designed to dynamically update point and distributional forecasts, when partial data in the most recent curve are observed.

References

R. J. Hyndman and M. S. Ullah (2007) "Robust forecasting of mortality and fertility rates: A functional data approach", Computational Statistics & Data Analysis, 51(10), 4942-4956. H. Shen and J. Z. Huang (2008) "Interday forecasting and intraday updating of call center arrivals", Manufacturing & Service Operations Management, 10(3), 391-410. H. Shen (2009) "On modeling and forecasting time series of curves", Technometrics, 51(3), 227-238. H. Shang and R. J. Hyndman (2009) "Nonparametric time series forecasting with dynamic updating", In R. S. Anderssen, R. D. Braddock and L.T.H. Newham (eds), 18th World IMACS Congress and MODSIM09 International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand and International Association for Mathematics and Computers in Simulation, July 2009, pp. 1552-1558. ISBN: 978-0-9758400-7-8. http://www.mssanz.org.au/modsim09/D11/shang.pdf

See Also

ftsm, forecast.ftsm, plot.fm, residuals.fm, summary.fm

Examples

dynupdate(data = ElNino, newdata = ElNino$y[1:4,57], 
          holdoutdata = ElNino$y[5:12,57], method = "ts")
dynupdate(data = ElNino, newdata = ElNino$y[1:4,57], 
          holdoutdata = ElNino$y[5:12,57], method = "block")
dynupdate(data = ElNino, newdata = ElNino$y[1:4,57], 
          holdoutdata = ElNino$y[5:12,57], method = "ols")
dynupdate(data = ElNino, newdata = ElNino$y[1:4,57], 
          holdoutdata = ElNino$y[5:12,57], method = "pls")
dynupdate(data = ElNino, newdata = ElNino$y[1:4,57], 
          holdoutdata = ElNino$y[5:12,57], method = "ridge")
dynupdate(data = ElNino, newdata = ElNino$y[1:4,57], 
          holdoutdata = ElNino$y[5:12,57], method = "block", interval = TRUE)