Predicting high-dimensional heterogeneous time series employing generalized local states

Modelling High Dimensional Time Series by Generalized Factor Models

We discuss and analyze generalized linear dynamic factor models. These models have been developed recently and they are used to model high dimensional time series in order to overcome the “curse of dimensionality”. The basic idea in factor models is to seperate “comovement” between the variables (caused by a relatively small number of factors) from individual (idiosyncratic) variation. Here fac...

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

Symbolic Analysis of High-Dimensional Time Series

In order to extract and to visualize qualitative information from a highdimensional time series, we apply ideas from symbolic dynamics. Counting certain ordinal patterns in the given series, we obtain a series of matrices whose entries are symbol frequencies. This matrix series is explored by simple methods from nominal statistics and information theory. The method is applied to detect and to v...

Modelling High-dimensional Time Series by Generalized Linear Dynamic Factor Models: an Introductory Survey

Factor models are used to condense high dimensional data consisting of many variables into a much smaller number of factors. Here we present an introductory survey to factor models for time series, where the factors represent the comovement between the single time series. Principal component analysis, linear dynamic factor models with idiosyncratic noise and generalized linear dynamic factor mo...