| ftsa-package | Functional Time Series Analysis |
| centre | Mean function, variance function, median function, trim mean function of functional data |
| CoDa_BayesNW | Compositional data analytic approach and nonparametric function-on-function regression for forecasting density |
| CoDa_FPCA | Compositional data analytic approach and functional principal component analysis for forecasting density |
| diff.fts | Differences of a functional time series |
| DJI_return | Dow Jones Industrial Average (DJIA) |
| dmfpca | Dynamic multilevel functional principal component analysis |
| dynamic_FLR | Dynamic updates via functional linear regression |
| dynupdate | Dynamic updates via BM, OLS, RR and PLS methods |
| error | Forecast error measure |
| ER_GR | Selection of the number of principal components |
| extract | Extract variables or observations |
| facf | Functional autocorrelation function |
| farforecast | Functional data forecasting through functional principal component autoregression |
| fbootstrap | Bootstrap independent and identically distributed functional data |
| forecast.ftsm | Forecast functional time series |
| forecast.hdfpca | Forecasting via a high-dimensional functional principal component regression |
| forecastfplsr | Forecast functional time series |
| fplsr | Functional partial least squares regression |
| ftsa | Functional Time Series Analysis |
| ftsm | Fit functional time series model |
| ftsmiterativeforecasts | Forecast functional time series |
| ftsmweightselect | Selection of the weight parameter used in the weighted functional time series model. |
| GAEVforecast | Fit a generalized additive extreme value model to the functional data with given basis numbers |
| hdfpca | High-dimensional functional principal component analysis |
| hd_data | Simulated high-dimensional functional time series |
| Horta_Ziegelmann_FPCA | Dynamic functional principal component analysis for density forecasting |
| is.fts | Test for functional time series |
| isfe.fts | Integrated Squared Forecast Error for models of various orders |
| long_run_covariance_estimation | Estimating long-run covariance function for a functional time series |
| LQDT_FPCA | Log quantile density transform |
| MAF_multivariate | Maximum autocorrelation factors |
| mean.fts | Mean functions for functional time series |
| median.fts | Median functions for functional time series |
| MFDM | Multilevel functional data method |
| MFPCA | Multilevel functional principal component analysis for clustering |
| mftsc | Multiple funtional time series clustering |
| pcscorebootstrapdata | Bootstrap independent and identically distributed functional data or 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 |
| plot.ftsm | Plot fitted model components for a functional time series model |
| plotfplsr | Plot fitted model components for a functional time series model |
| pm_10_GR | Particulate Matter Concentrations (pm10) |
| pm_10_GR_sqrt | Particulate Matter Concentrations (pm10) |
| quantile | Quantile |
| 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 |
| sim_ex_cluster | Simulated multiple sets of functional time series |
| sim_ex_cluster.smooth | Simulated multiple sets of functional time series |
| skew_t_fun | Skewed t distribution |
| stop_time_detect | Detection of the optimal stopping time in a curve time series |
| stop_time_sim_data | Simulated functional time series from a functional autoregression of order one |
| summary.fm | Summary for functional time series model |
| T_stationary | Testing stationarity of functional time series |
| var | Variance |
| var.default | Variance |
| var.fts | Variance functions for functional time series |