R: High-Dimensional Functional Time Series Analysis

# High-Dimensional Functional Time Series Analysis

## Documentation for package ‘hdftsa’ version 1.1

- DESCRIPTION file

## Help Pages

hdftsa-package | High-dimensional Functional Time Series Analysis
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).
dmfpca | Dynamic multilevel functional principal component analysis
forecast.hdfpca | Forecasting via a high-dimensional functional principal component regression
hdfpca | High-dimensional functional principal component analysis
hdftsa | High-dimensional Functional Time Series Analysis
hd_data | Simulated high-dimensional functional time series
MFPCA | Multilevel functional principal component analysis for clustering
mftsc | Multiple functional time series clustering
One_way_mean | One-way functional analysis of variance based on means
One_way_mean_residuals | High-dimensional functional time series decomposition into deterministic and functional residual components.
One_way_median_polish | One-way functional median polish from Sun and Genton (2012)
One_way_median_polish_residuals | High-dimensional functional time series decomposition into deterministic (from functional median polish of Sun and Genton (2012)), and functional residual components.
sim_ex_cluster | Simulated multiple sets of functional time series
sim_ex_cluster.smooth | Simulated multiple sets of functional time series
Two_way_mean | Functional analysis of variance fitted by means.
Two_way_mean_residuals | Functional time series decomposition into deterministic (functional analysis of variance fitted by means), and time-varying components (functional residuals).
Two_way_median_polish | Two-way functional median polish from Sun and Genton (2012)
Two_way_median_polish_residuals | Functional time series decomposition into deterministic (from functional median polish from Sun and Genton (2012)), and time-varying components (functional residuals).