CRAN_Status_Badge License: GPL v2 License: GPL v3

The Rfssa package provides the collection of necessary functions to implement functional singular spectrum analysis (FSSA)-based methods for analyzing univariate and multivariate functional time series (FTS). Univariate and multivariate FSSA are novel, non-parametric methods used to perform decomposition and reconstruction of univariate and multivariate FTS respectively. In addition, the FSSA-based routines may be performed on FTS whose variables are observed over a one or two-dimensional domain. Finally, one may perform FSSA recurrent or FSSA vector forecasting of univariate or multivariate funts observed over one-dimensional domains. Forecasting of funts whose variables are observed over domains of dimension greater than one is under development.


The use of the package begins by defining an `funts’ object by providing the constructor with the raw data, basis specifications, and grid specifications. We note that the FTS object may be univariate or multivariate and variables may be observed over one (curves) or two-dimensional (images) domains. Validity checking of the S4 object constructor inputs is included to help guide the user. The user may leverage the plot.funts method to visualize the funts object. A variety of plotting options are available for variables observed over a one-dimensional domain and a visuanimation is offered for variables observed over a two-dimensional domain. Next, the user provides the funts object and a chosen lag parameter to the FSSA routine (fssa) to obtain the decomposition. We note that the decomposition function leverages the RSpectra and RcppEigen R packages, and the Eigen C++ package to speed up the routine. The plot.fssa method may be used to visualize the results of the decomposition and to choose an appropriate grouping of the eigentriples for reconstruction (freconstruct) or forecasting (fforecast). The freconstruct routine can be used to reconstruct a list of funts objects specified by the grouping while the fforecast function returns a list of funts objects that contain predictions of the signals specified by the grouping. The user may also calculate the bootstrapped prediction interval for forecasts using the fpredinterval function. We note that when forecasting is performed, usually the user specifies one group that captures the assumed deterministic, extracted signal that is found within the FTS and all other modes of variation are excluded. We also note that currently, forecasting only supports FTS whose variables are observed over a one-dimensional domain with two-dimensional domain forecasting to be added in the future.

Other functionalities offered by the package include:



The reader should note that we do not utilize FTS plotting options in this README because of the large size of the resulting files. The reader should refer to the examples offered at the end of this README to see examples of how to apply the methodologies to real data.


You can install Rfssa from github with:

# install.packages("devtools")


The following links provide examples of how to run FSSA-related methods on real data:

FSSA Visualization

FSSA Decomposition

FSSA Reconstruction

FSSA Forecasting