Automated regime classification in multidimensional time series data using sliced Wasserstein k-means clustering
arXiv:2310.01285v2 Announce Type: replace-cross 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 comput
The paper builds on recent advancements in Wasserstein k-means clustering, applying it to multidimensional time series data, which is critical for financial and economic analysis as AI models become more sophisticated.
Improved methods for regime classification can enhance the accuracy of financial modeling, risk management, and economic forecasting, providing better insights into market dynamics.
The development of automated, more robust classification methods for complex time series data changes how financial and economic regimes can be identified and reacted to, moving beyond manually intensive or less precise techniques.
- · Quantitative finance firms
- · Data scientists
- · Financial analysts
- · High-frequency trading firms
- · Traditional statistical modeling approaches
- · Analysts relying on subjective regime identification
More accurate and automated identification of market regimes.
Potentially improved investment strategies and reduced financial risk through better predictive models.
Enhanced stability in financial markets as participants gain better understanding and responsiveness to systemic shifts.
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