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fdaACF: Autocorrelation Function for Functional Time Series
Quantify the serial correlation across lags of a given functional time series using the autocorrelation function and a partial autocorrelation function for functional time series proposed in Mestre et al. (2021) <doi:10.1016/j.csda.2020.107108>. The autocorrelation functions are based on the L2 norm of the lagged covariance operators of the series. Functions are available for estimating the distribution of the autocorrelation functions under the assumption of strong functional white noise.
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Package details |
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| Author | Guillermo Mestre Marcos [aut, cre], José Portela González [aut], Gregory Rice [aut], Antonio Muñoz San Roque [ctb], Estrella Alonso Pérez [ctb] |
| Maintainer | Guillermo Mestre Marcos <guillermo.mestre@comillas.edu> |
| License | GPL (>= 2) |
| Version | 1.0.0 |
| URL | https://github.com/GMestreM/fdaACF |
| Package repository | View on CRAN |
| Installation |
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