Changes in version 1.0.1                        

Fix minor bugs:

  - Fix the automatic selection of variable n_harm in function
    FTS_identification. The previous version obtained the first 10
    principal components of the functional time series and looked for
    the number of components neccesary to explain more than 95 % of the
    variance of the data. This approach presents several drawbacks:
    perhaps more than 10 PC are neccesary to explain more than 95% of
    the variance, or the series could have less than 10 discretization
    points. The updated version of function FTS_identification now uses
    the length of the input variable v to decide the number of PC to try
    and gives a warning message if 95 % of the variance cannot be
    explained by the maximum number of PC tried.

                 Changes in version 1.0.0 (2020-10-20)                  

New functionalities added in this release:

  - Add function FTS_identification. This function is a wrapper for
    functions obtain_FACF and obtain_FPACF, and shows both the FACF and
    the FPACF of a given functional time series.
  - Add citation information for the paper "Functional time series model
    identification and diagnosis by means of auto- and partial
    autocorrelation analysis" doi:10.1016/j.csda.2020.107108. This paper
    contains the theoretical foundations of the autocorrelation
    functions implemented in this package.
  - Fix minor bugs present in version 0.2.0.

                 Changes in version 0.2.0 (2020-08-11)                  

New functionalities added in this release:

  - Add function obtain_FPACF. This allows the user to estimate the
    functional partial autocorrelation function of a given functional
    time series.

                 Changes in version 0.1.0 (2020-01-24)                  

Initial release of the fdaACF library to CRAN.

This release contains the core functionalities of the fdaACF library.
Available at: https://CRAN.R-project.org/package=fdaACF