Model-based fuzzy time series clustering of conditional higher moments
Journal article
Cerqueti, R., Giacalone, M. and Mattera, R. (2021). Model-based fuzzy time series clustering of conditional higher moments. International Journal of Approximate Reasoning. 134, pp. 34-52. https://doi.org/10.1016/j.ijar.2021.03.011
Authors | Cerqueti, R., Giacalone, M. and Mattera, R. |
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Abstract | This paper develops a new time series clustering procedure allowing for heteroskedasticity, non-normality and model’s non-linearity. At this aim, we follow a fuzzy approach. Specifically, considering a Dynamic Conditional Score (DCS) model, we propose to cluster time series according to their estimated conditional moments via the Autocorrelation-based fuzzy C-means (A-FCM) algorithm. The DCS parametric modelingis appealing because of its generality and computational feasibility. The usefulness of the proposed procedure is illustrated using an experiment with simulated data and several empirical applications with financial time series assuming both linear and nonlinear models’ specification and under several assumptions about time series density function. |
Keywords | Fuzzy clustering; Dynamic conditional score; Conditional moments; Time series |
Year | 2021 |
Journal | International Journal of Approximate Reasoning |
Journal citation | 134, pp. 34-52 |
Publisher | Elsevier |
ISSN | 0888-613X |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.ijar.2021.03.011 |
Publication dates | |
22 Apr 2021 | |
Publication process dates | |
Accepted | 29 Mar 2021 |
Deposited | 16 Jun 2022 |
Accepted author manuscript | License File Access Level Open |
https://openresearch.lsbu.ac.uk/item/910qx
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