Unsupervised Bayesian Time-series Segmentation based on Linear Gaussian State-space Models

2008

Technical Report

ei

Unsupervised time-series segmentation in the general scenario in which the number of segment-types and segment boundaries are a priori unknown is a fundamental problem in many applications and requires an accurate segmentation model as well as a way of determining an appropriate number of segment-types. In most approaches, segmentation and determination of number of segment-types are addressed in two separate steps, since the segmentation model assumes a predefined number of segment-types. The determination of number of segment-types is thus achieved by training and comparing several separate models. In this paper, we take a Bayesian approach to a segmentation model based on linear Gaussian state-space models to achieve structure selection within the model. An appropriate prior distribution on the parameters is used to enforce a sparse parametrization, such that the model automatically selects the smallest number of underlying dynamical systems that explain the data well and a parsimonious structure for each dynamical system. As the resulting model is computationally intractable, we introduce a variational approximation, in which a reformulation of the problem enables to use an efficient inference algorithm.

@techreport{5312,
  title = {Unsupervised Bayesian Time-series Segmentation based on Linear Gaussian State-space Models},
  author = {Chiappa, S.},
  number = {171},
  organization = {Max-Planck-Gesellschaft},
  institution = {Max-Planck-Institute for Biological Cybernetics, Tübingen, Germany},
  school = {Biologische Kybernetik},
  month = jun,
  year = {2008},
  doi = {},
  month_numeric = {6}
}