publications
2026
- arXivCHASM: Online Changepoint Detection in Temporal and Cross-Variable Dependence2026
Changepoint detection identifies times when the generative process of a time series changes, with applications in healthcare, cybersecurity, and finance. In multivariate settings, changes in cross-variable and temporal dependence are particularly challenging to detect, as they are often less pronounced than shifts in marginal statistics such as the mean or variance. Existing methods detect changes using reconstruction error, which provides only an indirect measure of dynamical change, or rely on scalar functionals that may be too coarse to capture global structure. We introduce CHASM, an online nonparametric method that monitors the truncated eigenvalue sequence of the recursively estimated dynamic mode decomposition operator. Designing such an approach raises two challenges: the permutation invariance of eigendecompositions, resolved via optimal linear assignment, and the lack of online changepoint methods for multivariate complex-valued time series, addressed through a novel augmented monitoring scheme. We study the theoretical properties of the dynamics estimator under the canonical vector autoregressive model, which directly motivates our algorithmic design. The proposed method achieves competitive or superior performance to modern competitors across synthetic and real-world data sets, including challenging settings in video and text data. It is unsupervised, depends on a small number of interpretable parameters, and requires no distributional assumptions beyond finite moments, making it readily deployable across scientific domains.
2025
- under reviewOnline Changepoint Detection in Multivariate Seasonal Time Series via Dynamic Mode DecompositionVictor K. Khamesi, Dean A. Bodenham, and Edward A. K. Cohen2025
Extended version of the 2024 arXiv preprint (arXiv:2405.15576), including new theoretical results, refined methodology, and expanded empirical evaluation across synthetic and real-world data. Demonstrates superior performance over modern methods in detecting diverse change types, with successful applications to biomedical signals for different tasks (cardiac arrhythmias, epileptic seizures, gesture recognition).
2024
- arXivOnline Changepoint Detection via Dynamic Mode Decomposition2024
Detecting changes in data streams is a vital task in many applications. There is increasing interest in changepoint detection in the online setting, to enable real-time monitoring and support prompt responses and informed decision-making. Many approaches assume stationary sequences before encountering an abrupt change in the mean or variance. Notably less attention has focused on the challenging case where the monitored sequences exhibit trend, periodicity and seasonality. Dynamic mode decomposition is a data-driven dimensionality reduction technique that extracts the essential components of a dynamical system. We propose a changepoint detection method that leverages this technique to sequentially model the dynamics of a moving window of data and produce a low-rank reconstruction. A change is identified when there is a significant difference between this reconstruction and the observed data, and we provide theoretical justification for this approach. Extensive simulations demonstrate that our approach has superior detection performance compared to other methods for detecting small changes in mean, variance, periodicity, and second-order structure, among others, in data that exhibits seasonality. Results on real-world datasets also show excellent performance compared to contemporary approaches.
2022
- softwareocpdet: A Python Package for Online Changepoint Detection in Univariate and Multivariate DataVictor K. Khamesi2022
Open-source Python package implementing various online changepoint detection methods, including a self-supervised neural network approach for both univariate and multivariate data streams. Accompanies the Master’s Thesis: Self-supervised changepoint detection in streaming data using convolutional neural networks.