Unsupervised clustering of series using dynamic programming and neural processes
This work addresses the problem of model ambiguity and unknown exact models in series clustering for researchers in fields like physics or data analysis, but it appears incremental as it builds on prior work.
The paper tackles unsupervised clustering of multivariate series by integrating plausible models and data-driven approaches into an approximated model using neural processes, aiming to segment and cluster series into coherent blocks based on predefined model structures.
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.