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ftsa (version 6.5)
Functional Time Series Analysis
Description
Functions for visualizing, modeling, forecasting and hypothesis testing of functional time series.
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Install
install.packages('ftsa')
Monthly Downloads
13,631
Version
6.5
License
GPL-3
Maintainer
Han Lin Shang
Last Published
January 18th, 2025
Functions in ftsa (6.5)
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T_stationary
Testing stationarity of functional time series
centre
Mean function, variance function, median function, trim mean function of functional data
error
Forecast error measure
all_hmd_female_data
The US female log-mortality rate from 1959-2020 and 3 states (New York, California, Illinois).
dynupdate
Dynamic updates via BM, OLS, RR and PLS methods
dmfpca
Dynamic multilevel functional principal component analysis
extract
Extract variables or observations
forecast.hdfpca
Forecasting via a high-dimensional functional principal component regression
fbootstrap
Bootstrap independent and identically distributed functional data
ftsm
Fit functional time series model
ftsa-package
Functional Time Series Analysis
fplsr
Functional partial least squares regression
forecastfplsr
Forecast functional time series
farforecast
Functional data forecasting through functional principal component autoregression
forecast.ftsm
Forecast functional time series
ftsmiterativeforecasts
Forecast functional time series
facf
Functional autocorrelation function
hdfpca
High-dimensional functional principal component analysis
mftsc
Multiple funtional time series clustering
long_run_covariance_estimation
Estimating long-run covariance function for a functional time series
pcscorebootstrapdata
Bootstrap independent and identically distributed functional data or functional time series
ftsmweightselect
Selection of the weight parameter used in the weighted functional time series model.
mean.fts
Mean functions for functional time series
isfe.fts
Integrated Squared Forecast Error for models of various orders
median.fts
Median functions for functional time series
is.fts
Test for functional time series
plot.fmres
Plot residuals from a fitted functional model.
plot.ftsf
Plot fitted model components for a functional time series model
plot.fm
Plot fitted model components for a functional model
plot.ftsm
Plot fitted model components for a functional time series model
stop_time_detect
Detection of the optimal stopping time in a curve time series
hd_data
Simulated high-dimensional functional time series
sd
Standard deviation
residuals.fm
Compute residuals from a functional model
skew_t_fun
Skewed t distribution
quantile.fts
Quantile functions for functional time series
pm_10_GR
Particulate Matter Concentrations (pm10)
quantile
Quantile
summary.fm
Summary for functional time series model
plotfplsr
Plot fitted model components for a functional time series model
stop_time_sim_data
Simulated functional time series from a functional autoregression of order one
sd.fts
Standard deviation functions for functional time series
sim_ex_cluster
Simulated multiple sets of functional time series
var
Variance
var.fts
Variance functions for functional time series
ER_GR
Selection of the number of principal components
GAEVforecast
Fit a generalized additive extreme value model to the functional data with given basis numbers
CoDa_FPCA
Compositional data analytic approach and functional principal component analysis for forecasting density
dynamic_FLR
Dynamic updates via functional linear regression
MAF_multivariate
Maximum autocorrelation factors
CoDa_BayesNW
Compositional data analytic approach and nonparametric function-on-function regression for forecasting density
MFPCA
Multilevel functional principal component analysis for clustering
LQDT_FPCA
Log quantile density transform
MFDM
Multilevel functional data method
Horta_Ziegelmann_FPCA
Dynamic functional principal component analysis for density forecasting
all_hmd_male_data
The US male log-mortality rate from 1959-2020 and 3 states (New York, California, Illinois).
DJI_return
Dow Jones Industrial Average (DJIA)
diff.fts
Differences of a functional time series