Modeling Similarities Among Multi-Dimensional Financial Time Series

Modeling Financial Time Series With S - Plus

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

Multi-dimensional sparse time series: feature extraction

We show an analysis of multi-dimensional time series via entropy and statistical linguistic techniques. We define three markers encoding the behavior of the series, after it has been translated into a multi-dimensional symbolic sequence. The leading component and the trend of the series with respect to a mobile window analysis result from the entropy analysis and label the dynamical evolution o...

Shapelet Ensemble for Multi-dimensional Time Series

Time series shapelets are small subsequences that maximally differentiate classes of time series. Since the inception of shapelets, researchers have used shapelets for various data domains including anthropology and health care, and in the process suggested many efficient techniques for shapelet discovery. However, multi-dimensional time series data poses unique challenges to shapelet discovery...