# Greedy Gaussian Segmentation of Multivariate Time Series

arXiv: https://arxiv.org/abs/1610.07435 · HTML: https://mlsanity.com/paper/1610.07435/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Oct 2016.

## Built on by (most cited first, 7 of 7)

- [aeon: a Python toolkit for learning from time series](https://mlsanity.com/paper/2406.14231/) (2024)
- [Multimodal Fusion of EMG and Vision for Human Grasp Intent Inference in Prosthetic Hand Control](https://mlsanity.com/paper/2104.03893/) (2021)
- [Kernel Clustering with Sigmoid-based Regularization for Efficient Segmentation of Sequential Data](https://mlsanity.com/paper/2106.11541/) (2021)
- [An Empirical Evaluation of Factors Affecting SHAP Explanation of Time Series Classification](https://mlsanity.com/paper/2509.03649/) (2025)
- [Inference of Upcoming Human Grasp Using EMG During Reach-to-Grasp Movement](https://mlsanity.com/paper/2104.09627/) (2021)
- [Cross-user activity recognition using deep domain adaptation with temporal relation information](https://mlsanity.com/paper/2403.15424/) (2024)
- [Causality-driven Sequence Segmentation for Enhancing Multiphase Industrial Process Data Analysis and Soft Sensing](https://mlsanity.com/paper/2407.05954/) (2024)

## Abstract (arXiv)

We consider the problem of breaking a multivariate (vector) time series into segments over which the data is well explained as independent samples from a Gaussian distribution. We formulate this as a covariance-regularized maximum likelihood problem, which can be reduced to a combinatorial optimization problem of searching over the possible breakpoints, or segment boundaries. This problem can be solved using dynamic programming, with complexity that grows with the square of the time series length. We propose a heuristic method that approximately solves the problem in linear time with respect to this length, and always yields a locally optimal choice, in the sense that no change of any one breakpoint improves the objective. Our method, which we call greedy Gaussian segmentation (GGS), easily scales to problems with vectors of dimension over 1000 and time series of arbitrary length. We discuss methods that can be used to validate such a model using data, and also to automatically choose appropriate values of the two hyperparameters in the method. Finally, we illustrate our GGS approach on financial time series and Wikipedia text data.