Two novel distances for ordinal time series and their application to fuzzy clustering
Publication date
2023-05-23
Document type
Forschungsartikel
Author
Organisational unit
Publisher
Elsevier
Series or journal
Fuzzy sets and systems
ISSN
Periodical volume
468
Article ID
108590
Peer-reviewed
✅
Part of the university bibliography
✅
Language
English
Abstract
Time series clustering is a central machine learning task with applications in many fields. While the majority of the methods focus on real-valued time series, very few works consider series with discrete response. In this paper, the problem of clustering ordinal time series is addressed. To this aim, two novel distances between ordinal time series are introduced and used to construct fuzzy clustering procedures. Both metrics are functions of estimated cumulative probabilities, thus automatically taking advantage of the ordering inherent to the series' range. The resulting clustering algorithms are computationally efficient and able to group series generated from similar stochastic processes, reaching accurate results with series coming from a wide variety of models. Since the dynamics of the series may vary over the time, we adopt a fuzzy approach, thus enabling the procedures to locate each series into several clusters with different membership degrees. An extensive simulation study shows that the proposed methods outperform several alternative procedures. Weighted versions of the clustering algorithms are also presented and their advantages with respect to the original methods are discussed. Two specific applications involving economic time series illustrate the usefulness of the proposed approaches.
Description
This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Version
Published version
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