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

TL;DR

This paper extends Wasserstein k-means clustering to multidimensional time series using sliced Wasserstein distance, demonstrating its effectiveness in identifying regimes in synthetic and real financial data.

Contribution

It introduces sliced Wasserstein k-means for multidimensional time series and validates its performance in regime detection on synthetic and financial datasets.

Findings

Effective in synthetic regime detection

Successfully applied to foreign exchange data

Provides metrics for clustering quality

Abstract

Recent work has proposed Wasserstein k-means (Wk-means) clustering as a powerful method to identify 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 study the dynamics of the algorithm and investigate how varying different hyperparameters impacts the performance of the clustering algorithm for different random initialisations. We compute simple metrics that we find are useful in identifying high-quality clusterings. Then, we 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 detection in multidimensional time series data, using synthetic data to demonstrate the validity of the approach. Finally, we show that the sWk-means method is effective in identifying 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.