Online Regression for Data with Changepoints Using Gaussian Processes and Reusable Models

Robert C. Grande, Thomas J. Walsh, Girish Chowdhary, Sarah Ferguson, Jonathan P. How

Research output: Contribution to journalArticlepeer-review


Many prediction, decision-making, and control architectures rely on online learned Gaussian process (GP) models. However, most existing GP regression algorithms assume a single generative model, leading to poor predictive performance when the data are nonstationary, i.e., generated from multiple switching processes. Furthermore, existing methods for GP regression over nonstationary data require significant computation, do not come with provable guarantees on correctness and speed, and many only work in batch settings, making them ill-suited for real-time prediction. We present an efficient online GP framework, GP-non-Bayesian clustering (GP-NBC), which addresses these computational and theoretical issues, allowing for real-time changepoint detection and regression using GPS. Our empirical results on two real-world data sets and two synthetic data set show that GP-NBC outperforms state-of-the-art methods for nonstationary regression in terms of both regression error and computation. For example, it outperforms Dirichlet process GP clustering with Gibbs sampling by 98% in computation time reduction while the mean absolute error is comparable.

Original languageEnglish (US)
Article number7491276
Pages (from-to)2115-2128
Number of pages14
JournalIEEE Transactions on Neural Networks and Learning Systems
Issue number9
StatePublished - Sep 2017


  • Changepoint detection (CPD)
  • Gaussian processes (GPS)
  • online

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence


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