Abstract In this paper, we propose a new approach for clustering time series showing similar time-varying moments. At this aim, we compute a dissimilarity measure assuming that the estimated conditional moments are continuous functions indexed by time. Conditional moments based clustering allows to obtain different classifications according to the data distribution’s parameters. We show the usefulness of the proposed clustering procedure with an application to the financial time series in the DAX30 index
Conditional moments based time series cluster analysis / Mattera, Raffaele; Scepi, Germana. - (2021), pp. 1593-1598. (Intervento presentato al convegno SIS 2021 tenutosi a Pisa nel 21-25 giugno).
Conditional moments based time series cluster analysis
Mattera Raffaele;Scepi Germana
2021
Abstract
Abstract In this paper, we propose a new approach for clustering time series showing similar time-varying moments. At this aim, we compute a dissimilarity measure assuming that the estimated conditional moments are continuous functions indexed by time. Conditional moments based clustering allows to obtain different classifications according to the data distribution’s parameters. We show the usefulness of the proposed clustering procedure with an application to the financial time series in the DAX30 indexI documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
