The decentralized response is forecasted by multiplying the estimated regression coefficient with the new decentralized predictor
forecastfplsr(object, components, h)
An object of class
Number of optimal components.
fts class object, containing forecasts of responses.
Han Lin Shang
R. J. Hyndman and H. L. Shang (2009) "Forecasting functional time series" (with discussion), Journal of the Korean Statistical Society, 38(3), 199-221.
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# A set of functions are decomposed by functional partial least squares decomposition. # By forecasting univariate partial least squares scores, the forecasted curves are # obtained by multiplying the forecasted scores by fixed functional partial least # squares function plus fixed mean function. forecastfplsr(object = ElNino_ERSST_region_1and2, components = 2, h = 5)
Loading required package: forecast Loading required package: rainbow Loading required package: MASS Loading required package: pcaPP Loading required package: sde Loading required package: stats4 Loading required package: fda Loading required package: splines Loading required package: Matrix Attaching package: 'fda' The following object is masked from 'package:forecast': fourier The following object is masked from 'package:graphics': matplot Loading required package: zoo Attaching package: 'zoo' The following objects are masked from 'package:base': as.Date, as.Date.numeric sde 2.0.15 Companion package to the book 'Simulation and Inference for Stochastic Differential Equations With R Examples' Iacus, Springer NY, (2008) To check the errata corrige of the book, type vignette("sde.errata") Attaching package: 'ftsa' The following objects are masked from 'package:stats': sd, var Functional time series y: Sea surface temperature x: Month
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