Learn R Programming

ftsa (version 6.4)

Functional Time Series Analysis

Description

Functions for visualizing, modeling, forecasting and hypothesis testing of functional time series.

Copy Link

Version

Install

install.packages('ftsa')

Monthly Downloads

2,259

Version

6.4

License

GPL-3

Maintainer

Last Published

January 23rd, 2024

Functions in ftsa (6.4)

Two_way_Residuals

Functional time series decomposition into deterministic (from functional median polish from Sun and Genton (2012)), and time-varying components (functional residuals).
centre

Mean function, variance function, median function, trim mean function of functional data
facf

Functional autocorrelation function
diff.fts

Differences of a functional time series
error

Forecast error measure
dmfpca

Dynamic multilevel functional principal component analysis
One_way_Residuals

Functional time series decomposition into deterministic (from functional median polish of Sun and Genton (2012)), and functional residual components.
fplsr

Functional partial least squares regression
ftsa-package

Functional Time Series Analysis
Two_way_Residuals_means

Functional time series decomposition into deterministic (functional analysis of variance fitted by means), and time-varying components (functional residuals).
MFPCA

Multilevel functional principal component analysis for clustering
fbootstrap

Bootstrap independent and identically distributed functional data
ftsm

Fit functional time series model
ftsmiterativeforecasts

Forecast functional time series
T_stationary

Testing stationarity of functional time series
hdfpca

High-dimensional functional principal component analysis
extract

Extract variables or observations
forecast.ftsm

Forecast functional time series
farforecast

Functional data forecasting through functional principal component autoregression
is.fts

Test for functional time series
dynupdate

Dynamic updates via BM, OLS, RR and PLS methods
isfe.fts

Integrated Squared Forecast Error for models of various orders
plot.fm

Plot fitted model components for a functional model
dynamic_FLR

Dynamic updates via functional linear regression
mftsc

Multiple funtional time series clustering
quantile

Quantile
ftsmweightselect

Selection of the weight parameter used in the weighted functional time series model.
pcscorebootstrapdata

Bootstrap independent and identically distributed functional data or functional time series
hd_data

Simulated high-dimensional functional time series
long_run_covariance_estimation

Estimating long-run covariance function for a functional time series
mean.fts

Mean functions for functional time series
var

Variance
median.fts

Median functions for functional time series
var.fts

Variance functions for functional time series
sim_ex_cluster

Simulated multiple sets of functional time series
sd.fts

Standard deviation functions for functional time series
quantile.fts

Quantile functions for functional time series
residuals.fm

Compute residuals from a functional model
stop_time_sim_data

Simulated functional time series from a functional autoregression of order one
plot.fmres

Plot residuals from a fitted functional model.
pm_10_GR

Particulate Matter Concentrations (pm10)
plot.ftsf

Plot fitted model components for a functional time series model
stop_time_detect

Detection of the optimal stopping time in a curve time series
skew_t_fun

Skewed t distribution
plotfplsr

Plot fitted model components for a functional time series model
forecast.hdfpca

Forecasting via a high-dimensional functional principal component regression
sd

Standard deviation
plot.ftsm

Plot fitted model components for a functional time series model
summary.fm

Summary for functional time series model
forecastfplsr

Forecast functional time series
DJI_return

Dow Jones Industrial Average (DJIA)
MAF_multivariate

Maximum autocorrelation factors
MFDM

Multilevel functional data method
CoDa_BayesNW

Compositional data analytic approach and nonparametric function-on-function regression for forecasting density
all_hmd_female_data

The US female log-mortality rate from 1959-2020 and 3 states (New York, California, Illinois).
FANOVA

Functional analysis of variance fitted by means.
all_hmd_male_data

The US male log-mortality rate from 1959-2020 and 3 states (New York, California, Illinois).
GAEVforecast

Fit a generalized additive extreme value model to the functional data with given basis numbers
One_way_median_polish

One-way functional median polish from Sun and Genton (2012)
LQDT_FPCA

Log quantile density transform
CoDa_FPCA

Compositional data analytic approach and functional principal component analysis for forecasting density
Horta_Ziegelmann_FPCA

Dynamic functional principal component analysis for density forecasting
Two_way_median_polish

Two-way functional median polish from Sun and Genton (2012)
ER_GR

Selection of the number of principal components