# Fast nonparametric clustering of structured time-series

arXiv: https://arxiv.org/abs/1401.1605 · HTML: https://mlsanity.com/paper/1401.1605/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jan 2014.

## Built on by (most cited first, 1 of 1)

- [Bayesian Nonparametric Dynamical Clustering of Time Series](https://mlsanity.com/paper/2510.06919/) (2025)

## Abstract (arXiv)

In this publication, we combine two Bayesian non-parametric models: the Gaussian Process (GP) and the Dirichlet Process (DP). Our innovation in the GP model is to introduce a variation on the GP prior which enables us to model structured time-series data, i.e. data containing groups where we wish to model inter- and intra-group variability. Our innovation in the DP model is an implementation of a new fast collapsed variational inference procedure which enables us to optimize our variationala pproximation significantly faster than standard VB approaches. In a biological time series application we show how our model better captures salient features of the data, leading to better consistency with existing biological classifications, while the associated inference algorithm provides a twofold speed-up over EM-based variational inference.