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 index
2021
9788891927361
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).
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/853570
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