G. Mestre Marcos, J. Portela González, A. Muñoz San Roque, E. Alonso Pérez
Quantifying the serial correlation across lags is a crucial step in the identification and diagnosis of a model for scalar time series, where the autocorrelation and partial autocorrelation functions of the time series are the most common tools used for this purpose. A lagged autocorrelation function for functional time series is proposed, which is based on the L2 norm of the lagged covariance operators of the series. Diagnostic plots utilizing large sample results for the autocorrelation function of a strong white noise sequence are proposed as a tool for selecting the order and assessing the adequacy of functional SARIMAX models. The proposed methods are studied in numerical simulations with both white noise and dependent functional processes, which show that the structure of the processes can be diagnosed using the techniques described. The applicability of the method is illustrated via applications to a real-world datasets of spanish electricity price profiles.
Palabras clave: Functional time series, Model diagnosis, Autocovariance
Programado
GT6-3 Análisis de Datos Funcionales
5 de septiembre de 2019 14:45
I3L9. Edificio Georgina Blanes