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.
| Subjects: | Computational Finance (q-fin.CP); Machine Learning (cs.LG); Mathematical Finance (q-fin.MF); Machine Learning (stat.ML) |
| Cite as: | arXiv:2310.01285 [q-fin.CP] |
| (or arXiv:2310.01285v2 [q-fin.CP] for this version) | |
| https://doi.org/10.48550/arXiv.2310.01285 arXiv-issued DOI via DataCite |
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| Journal reference: | Data Science in Finance and Economics 2025, Volume 5, Issue 3: 387-418 |
| Related DOI: | https://doi.org/10.3934/DSFE.2025016
DOI(s) linking to related resources |
From: James Hamp [view email]
[v1]
Mon, 2 Oct 2023 15:37:56 UTC (10,575 KB)
[v2]
Sun, 24 May 2026 17:21:29 UTC (10,561 KB)
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