2015/02/11 by Alexander Vandenberg‐Rodes, Alexander Vandenberg-Rodes, Vandenberg-Rodes, Alexander +2 · 1 citation
Computer Science · Mathematics · Physics and Astronomy · #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Machine Learning (stat.ML) #Neural Networks and Applications #Statistical Mechanics and Entropy #stat.ML
paper · pdf · doi:10.48550/arxiv.1502.03466
10 pages
arxiv created 2015/02/11 · openalex publication_date 2015/02/11 · arxiv updated 2015/02/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
For the challenging task of modeling multivariate time series, we propose a new class of models that use dependent Matérn processes to capture the underlying structure of data, explain their interdependencies, and predict their unknown values. Although similar models have been proposed in the econometric, statistics, and machine learning literature, our approach has several advantages that distinguish it from existing methods: 1) it is flexible to provide high prediction accuracy, yet its complexity is controlled to avoid overfitting; 2) its interpretability separates it from black-box methods; 3) finally, its computational efficiency makes it scalable for high-dimensional time series. In this paper, we use several simulated and real data sets to illustrate these advantages. We will also briefly discuss some extensions of our model.