Automated regime classification in multidimensional time series data using sliced Wasserstein k-means clustering

Luan, Qinmeng and Hamp, James (2025) Automated regime classification in multidimensional time series data using sliced Wasserstein k-means clustering. Data Science in Finance and Economics, 5 (3). 387 - 418.

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Abstract

Recent work has proposed Wasserstein k-means (Wk-means) clustering as a powerful method to classify regimes in time series data, and one-dimensional asset returns in particular. In this paper, we begin by studying in detail the behaviour of the Wasserstein k-means clustering algorithm applied to synthetic one-dimensional time series data. We extend the previous work by studying, in detail, the dynamics of the clustering algorithm and how varying the hyperparameters impacts the performance over different random initialisations. We compute simple metrics that we find to be useful in identifying high-quality clusterings. We then extend the technique of Wasserstein k-means clustering to multidimensional time series data by approximating the multidimensional Wasserstein distance as a sliced Wasserstein distance, resulting in a method we call 'sliced Wasserstein k-means (sWk-means) clustering'. We apply the sWk-means clustering method to the problem of automated regime classification in multidimensional time series data, using synthetic data to demonstrate the validity and effectiveness of the approach. Finally, we show that the sWk-means method is able to identify distinct market regimes in real multidimensional financial time series, using publicly available foreign exchange spot rate data as a case study. We conclude with remarks about some limitations of our approach and potential complementary or alternative approaches.

Item Type:	Article
Additional Information:	© 2025 the Author(s)
Divisions:	Data Science Institute
Subjects:	Q Science > QA Mathematics H Social Sciences > HG Finance
JEL classification:	C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods: General > C14 - Semiparametric and Nonparametric Methods C - Mathematical and Quantitative Methods > C6 - Mathematical Methods and Programming > C63 - Computational Techniques
Date Deposited:	16 Sep 2025 08:24
Last Modified:	16 Sep 2025 08:24
URI:	http://eprints.lse.ac.uk/id/eprint/129537

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