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In this work, we consider time series of daily concentrations of PM \(_{10}\) monitored in Lombardia and Emilia-Romagna during 2018. With the aim of clustering those spatial time series, we propose a Bayesian nonparametric mixture of autoregressive processes and assume as mixing measure a spatial product partition model. We focus on the implementation of this model into BayesMix, a new C++ library for Bayesian inference on nonparametric mixture models via Markov Chain Monte Carlo. The main feature of this library is its extensibility, which guarantees a seamless integration of new classes of mixture models, like the one we introduce in this paper, without compromising efficiency.

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Model-Based Clustering of Spatial Time Series Through the BayesMix library

  • 会议论文

  • 发表日期：

    2025年

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  • Matteo Gianella,
  • Alessandra Guglielmi

摘要

In this work, we consider time series of daily concentrations of PM \(_{10}\) monitored in Lombardia and Emilia-Romagna during 2018. With the aim of clustering those spatial time series, we propose a Bayesian nonparametric mixture of autoregressive processes and assume as mixing measure a spatial product partition model. We focus on the implementation of this model into BayesMix, a new C++ library for Bayesian inference on nonparametric mixture models via Markov Chain Monte Carlo. The main feature of this library is its extensibility, which guarantees a seamless integration of new classes of mixture models, like the one we introduce in this paper, without compromising efficiency.