# Time Series Clustering with an EM algorithm for Mixtures of Linear Gaussian State Space Models

arXiv: https://arxiv.org/abs/2208.11907 · HTML: https://mlsanity.com/paper/2208.11907/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 39 references that are in the map.
- First posted on arXiv 25 Aug 2022.

## Builds on (most cited first, 3 of 3)

- [Composing graphical models with neural networks for structured representations and fast inference](https://mlsanity.com/paper/1603.06277/) (2016)
- [Clustering Time Series with Nonlinear Dynamics: A Bayesian Non-Parametric and Particle-Based Approach](https://mlsanity.com/paper/1810.09920/) (2018)
- [Clustering Discrete-Valued Time Series](https://mlsanity.com/paper/1901.09249/) (2019)

## Built on by (most cited first, 5 of 5)

- [Fuzzy Cluster-Aware Contrastive Clustering for Time Series](https://mlsanity.com/paper/2503.22211/) (2025)
- [Clustering Method for Time-Series Images Using Quantum-Inspired Computing Technology](https://mlsanity.com/paper/2305.16656/) (2023)
- [FR-Mamba: Time-Series Physical Field Reconstruction Based on State Space Model](https://mlsanity.com/paper/2505.16083/) (2025)
- [Time Series Clustering with General State Space Models via Stochastic Variational Inference](https://mlsanity.com/paper/2407.00429/) (2024)
- [Language Markers of Emotion Flexibility Predict Depression and Anxiety Treatment Outcomes](https://mlsanity.com/paper/2601.07961/) (2026)

## Abstract (arXiv)

In this paper, we consider the task of clustering a set of individual time series while modeling each cluster, that is, model-based time series clustering. The task requires a parametric model with sufficient flexibility to describe the dynamics in various time series. To address this problem, we propose a novel model-based time series clustering method with mixtures of linear Gaussian state space models, which have high flexibility. The proposed method uses a new expectation-maximization algorithm for the mixture model to estimate the model parameters, and determines the number of clusters using the Bayesian information criterion. Experiments on a simulated dataset demonstrate the effectiveness of the method in clustering, parameter estimation, and model selection. The method is applied to real datasets commonly used to evaluate time series clustering methods. Results showed that the proposed method produces clustering results that are as accurate or more accurate than those obtained using previous methods.