Functional time series analysis

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Documentation for package ‘ftsa’ version 1.3

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ftsa-package Functional time series analysis
cm.spline Monotonic interpolating splines
diff.fts Differences of a functional time series
dynupdate Dynamic updates via BM, OLS, RR and PLS methods
dynupdateweightselect Selecting an optimal tuning parameter for the PLS and RR methods
error Forecast error measure
extract Extract variables or observations
fbootstrap Bootstrap independent and identically distributed functional data
forecast.ftsm Forecast functional time series
fplsr Functional partial least squares regression
fplsrweightselect Selecting an optimal weight parameter for the weighted functional partial least squares regression
ftsm Fit functional time series model
ftsmweightselect Selecting an optimal weight parameter for the weighted functional principal component regression
is.fts Test for functional time series
isfe.fts Integrated Squared Forecast Error for models of various orders
mean.fts Mean functions for functional time series
median Median
median.default Median
median.fts Median functions for functional time series
plot.fm Plot fitted model components for a functional model
plot.fmres Plot residuals from a fitted functional model.
plot.ftsf Plot fitted model components for a functional time series model
quantile.fts Quantile functions for functional time series
residuals.fm Compute residuals from a functional model
sd Standard deviation
sd.default Standard deviation
sd.fts Standard deviation functions for functional time series
summary.fm Summary for functional time series model
var Variance
var.default Variance
var.fts Variance functions for functional time series