← Back to all news
arXiv Machine Learning By Guilherme Piantino, Hedibert F. Lopes

Time-partitioning Gaussian Process Mixture Model

Read the original on arXiv Machine Learning →

The Time-partitioning Gaussian Process Mixture Model (TPGPMM) is a Bayesian nonparametric framework that identifies persistent regimes and models nonstationary dependence in time series. It uses an adaptive Pitman–Yor prior on contiguous segmentations combined with regime-specific Gaussian processes, and learns change points and hyperparameters via a split–merge Metropolis-within-Gibbs sampler. Experiments on the CBOE Volatility Index and the 10‑year U.S. Treasury yield recover regime structures aligned with major market and monetary‑policy events, while variants that estimate observation noise improve predictive accuracy but may reduce sparsity or event alignment.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

Related stories

arXiv Machine Learning
Oct 12

Scan-Parallel Variational Inference for event-driven Latent Jump Models

The paper introduces latent jump models (LJMs) for continuous-time latent state-space modeling, where the latent state evolves deterministically between Poisson-driven events and experiences learned instantaneous jumps at each event. It presents a tractable event-augmented ELBO and a novel Scan-Parallel Variational Inference (SPVI) method that samples the full latent event configuration before evaluating state recurrence, enabling logarithmic temporal depth and fast, scalable training. Experiments on synthetic and real irregular time series show that LJMs achieve competitive forecast accuracy with fewer latent updates and train one to two orders of magnitude faster than sequential baselines.

By Vansh Bansal, Min Chen, James G. Scott
More like this →
arXiv Machine Learning
Sep 30

Gaussian Mixture Copula Processes for Irregular Time Series

arXiv:2605.23632v2 Announce Type: replace Abstract: We introduce Gaussian Mixture Copula Processes (GMCP), a conditional copula process for irregularly sampled multivariate time series (IMTS) that is...

By Christian Kl\"otergens, Tom Hanika, Lars Schmidt-Thieme, Vijaya Krishna Yalavarthi
More like this →