Package: ftsa 6.4

Han Lin Shang

ftsa: Functional Time Series Analysis

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

Authors:Rob Hyndman [aut], Han Lin Shang [aut, cre, cph]

ftsa_6.4.tar.gz
ftsa_6.4.tar.gz(r-4.5-noble)ftsa_6.4.tar.gz(r-4.4-noble)
ftsa_6.4.tgz(r-4.4-emscripten)ftsa_6.4.tgz(r-4.3-emscripten)
ftsa.pdf |ftsa.html✨
ftsa/json (API)

# Install 'ftsa' in R:
install.packages('ftsa', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Datasets:
  • DJI_return - Dow Jones Industrial Average
  • all_hmd_female_data - The US female log-mortality rate from 1959-2020 and 3 states (New York, California, Illinois).
  • all_hmd_male_data - The US male log-mortality rate from 1959-2020 and 3 states (New York, California, Illinois).
  • hd_data - Simulated high-dimensional functional time series
  • pm_10_GR - Particulate Matter Concentrations
  • pm_10_GR_sqrt - Particulate Matter Concentrations
  • sim_ex_cluster - Simulated multiple sets of functional time series
  • sim_ex_cluster.smooth - Simulated multiple sets of functional time series

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

4.58 score 6 stars 9 packages 93 scripts 2.5k downloads 59 exports 135 dependencies

Last updated 10 months agofrom:6e33317468. Checks:OK: 1 NOTE: 1. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 01 2024
R-4.5-linuxNOTENov 01 2024

Exports:centreCoDa_BayesNWCoDa_FPCAdiff.ftsdmfpcadynamic_FLRdynupdateER_GRerrorextractfacfFANOVAfarforecastfbootstrapforecast.ftsmforecast.hdfpcaforecastfplsrfplsrftsmftsmiterativeforecastsftsmweightselectGAEVforecasthdfpcaHorta_Ziegelmann_FPCAis.ftsisfe.ftslong_run_covariance_estimationLQDT_FPCAMAF_multivariatemean.ftsmedian.ftsMFDMMFPCAmftscOne_way_median_polishOne_way_Residualspcscorebootstrapdataplot.fmplot.fmresplot.ftsfplot.ftsmplotfplsrquantilequantile.ftsresiduals.fmsdsd.defaultsd.ftsskew_t_funstop_time_detectstop_time_sim_datasummary.fmT_stationaryTwo_way_median_polishTwo_way_ResidualsTwo_way_Residuals_meansvarvar.defaultvar.fts

Dependencies:abindashbackportsbase64encbitopsbootbslibcachemcheckmateclasscliclustercolorspacecurlcvardata.tabledeSolvedigeste1071ecpevaluateevgamfansifarverfastICAfastmapfBasicsfdafdapacefdsfGarchFNNfontawesomeforecastforeignFormulafracdifffsgbutilsgenericsgeometryggplot2glueGPArotationgridExtragssgtablehdrcdehighrHmischtmlTablehtmltoolshtmlwidgetsisobandjquerylibjsonlitekernlabKernSmoothknitrkslabelingLaplacesDemonlatticelifecyclelinprogLmomentslmtestlocfitlpSolvemagicmagrittrMASSMatrixmclustmemoisemgcvmimemnormtmulticoolmunsellmvtnormnlmennetnumDerivpcaPPpdfClusterpillarpkgconfigpracmaproxypsychquadprogquantmodR6rainbowrappdirsrbibutilsRColorBrewerRcppRcppArmadilloRcppEigenRcppProgressRCurlRdpackrlangrmarkdownROOPSDrpartrstudioapisandwichsassscalessdespatialstablediststringistringrstrucchangetibbletimeDatetimeSeriestinytextseriesTTRurcautf8varsvctrsviridisviridisLitewithrxfunxtsyamlzoo

Readme and manuals

Help Manual

Help pageTopics
Functional Time Series Analysisftsa-package ftsa
The US female log-mortality rate from 1959-2020 and 3 states (New York, California, Illinois).all_hmd_female_data
The US male log-mortality rate from 1959-2020 and 3 states (New York, California, Illinois).all_hmd_male_data
Mean function, variance function, median function, trim mean function of functional datacentre
Compositional data analytic approach and nonparametric function-on-function regression for forecasting densityCoDa_BayesNW
Compositional data analytic approach and functional principal component analysis for forecasting densityCoDa_FPCA
Differences of a functional time seriesdiff.fts
Dow Jones Industrial Average (DJIA)DJI_return
Dynamic multilevel functional principal component analysisdmfpca
Dynamic updates via functional linear regressiondynamic_FLR
Dynamic updates via BM, OLS, RR and PLS methodsdynupdate
Selection of the number of principal componentsER_GR
Forecast error measureerror
Extract variables or observationsextract
Functional autocorrelation functionfacf
Functional analysis of variance fitted by means.FANOVA
Functional data forecasting through functional principal component autoregressionfarforecast
Bootstrap independent and identically distributed functional datafbootstrap
Forecast functional time seriesforecast.ftsm
Forecasting via a high-dimensional functional principal component regressionforecast.hdfpca
Forecast functional time seriesforecastfplsr
Functional partial least squares regressionfplsr
Fit functional time series modelftsm
Forecast functional time seriesftsmiterativeforecasts
Selection of the weight parameter used in the weighted functional time series model.ftsmweightselect
Fit a generalized additive extreme value model to the functional data with given basis numbersGAEVforecast
Simulated high-dimensional functional time serieshd_data
High-dimensional functional principal component analysishdfpca
Dynamic functional principal component analysis for density forecastingHorta_Ziegelmann_FPCA
Test for functional time seriesis.fts
Integrated Squared Forecast Error for models of various ordersisfe.fts
Estimating long-run covariance function for a functional time serieslong_run_covariance_estimation
Log quantile density transformLQDT_FPCA
Maximum autocorrelation factorsMAF_multivariate
Mean functions for functional time seriesmean.fts
Median functions for functional time seriesmedian.fts
Multilevel functional data methodMFDM
Multilevel functional principal component analysis for clusteringMFPCA
Multiple funtional time series clusteringmftsc
One-way functional median polish from Sun and Genton (2012)One_way_median_polish
Functional time series decomposition into deterministic (from functional median polish of Sun and Genton (2012)), and functional residual components.One_way_Residuals
Bootstrap independent and identically distributed functional data or functional time seriespcscorebootstrapdata
Plot fitted model components for a functional modelplot.fm
Plot residuals from a fitted functional model.plot.fmres
Plot fitted model components for a functional time series modelplot.ftsf
Plot fitted model components for a functional time series modelplot.ftsm
Plot fitted model components for a functional time series modelplotfplsr
Particulate Matter Concentrations (pm10)pm_10_GR pm_10_GR_sqrt
Quantilequantile
Quantile functions for functional time seriesquantile.fts
Compute residuals from a functional modelresiduals.fm
Standard deviationsd sd.default
Standard deviation functions for functional time seriessd.fts
Simulated multiple sets of functional time seriessim_ex_cluster sim_ex_cluster.smooth
Skewed t distributionskew_t_fun
Detection of the optimal stopping time in a curve time seriesstop_time_detect
Simulated functional time series from a functional autoregression of order onestop_time_sim_data
Summary for functional time series modelsummary.fm
Testing stationarity of functional time seriesT_stationary
Two-way functional median polish from Sun and Genton (2012)Two_way_median_polish
Functional time series decomposition into deterministic (from functional median polish from Sun and Genton (2012)), and time-varying components (functional residuals).Two_way_Residuals
Functional time series decomposition into deterministic (functional analysis of variance fitted by means), and time-varying components (functional residuals).Two_way_Residuals_means
Variancevar var.default
Variance functions for functional time seriesvar.fts