Hybrid Latent Variable Modeling of High Dimensional Time Series Data

Modeling High Dimensional Time Series

This paper investigates the effectiveness of the recently proposed Gaussian Process Dynamical Model (GPDM) on high dimensional chaotic time series. The GPDM takes a Bayesian approach to modeling high-dimensional time series data, using the Gaussian process Latent Variable model (GPLVM) for nonlinear dimensionality reduction combined with a nonlinear dynamical model in latent space. The GPDM is ...

Compact modeling of high-dimensional time-series

Estimation of Latent Factors for High-Dimensional Time Series∗

This paper deals with the dimension reduction of high-dimensional time series based on a lowerdimensional factor process. In particular we allow the dimension of time series N to be as large as, or even larger than, the length of observed time series (also refereed as the sample size) T . The estimation of the factor loading matrix and the factor process itself is carried out via an eigenanalys...

Latent variable modeling

doi: 10.3969/j.issn.1002-0829.2012.02.010 National Center for Research on Evaluation, Standards, and Student Testing, University of California, Los Angeles, CA, USA *Correspondence: [email protected] A latent variable model, as the name suggests, is a statistical model that contains latent, that is, unobserved, variables. Their roots go back to Spearman’s 1904 seminal work on factor analysis, which...

Gaussian Process Latent Variable Models for Dimensionality Reduction and Time Series Modeling

Time series data of high dimensions are frequently encountered in fields like robotics, computer vision, economics and motion capture. In this survey paper we look first at Gaussian Process Latent Variable Model (GPLVM) which is a probabilistic nonlinear dimensionality reduction method. Further we discuss Gaussian Process Dynamical Model (GPDMs) which are based GPLVM. GPDM is a probabilistic ap...