Unsupervised clustering of series using dynamic programming and neural processes

TL;DR

This paper proposes a novel framework combining neural processes with dynamic programming for unsupervised clustering of multivariate series, aiming to handle multiple plausible models and unknown underlying data models.

Contribution

It introduces a general approach integrating neural processes into a dynamic programming-based clustering algorithm for series segmentation.

Findings

Framework effectively incorporates multiple models

Neural processes improve model approximation

Enables data-driven and model-based clustering

Abstract

Following the work of arXiv:2101.09512, we are interested in clustering a given multi-variate series in an unsupervised manner. We would like to segment and cluster the series such that the resulting blocks present in each cluster are coherent with respect to a predefined model structure (e.g. a physics model with a functional form defined by a number of parameters). However, such approach might have its limitation, partly because there may exist multiple models that describe the same data, and partly because the exact model behind the data may not immediately known. Hence, it is useful to establish a general framework that enables the integration of plausible models and also accommodates data-driven approach into one approximated model to assist the clustering task. Hence, in this work, we investigate the use of neural processes to build the approximated model while yielding the same assumptions required by the algorithm presented in arXiv:2101.09512.