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- W4287248669 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 modelling is 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." @default.
- W4287248669 created "2022-07-25" @default.
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- W4287248669 date "2021-04-01" @default.
- W4287248669 modified "2023-09-26" @default.
- W4287248669 title "Model-based fuzzy time series clustering of conditional higher moments" @default.
- W4287248669 doi "https://doi.org/10.48550/arxiv.2104.00271" @default.
- W4287248669 hasPublicationYear "2021" @default.
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