By Topic

Graphical Models for Time-Series

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)

Time-series analysis is central to many problems in signal processing, including acoustics, image processing, vision, tracking, information retrieval, and finance, to name a few. Because of the wide base of application areas, having a common description of the models is useful in transferring ideas between the various communities. Graphical models provide a compact way to represent such models and thereby rapidly transfer ideas. We will discuss briefly how classical timeseries models such as Kalman filters and hidden Markov models (HMMs) can be represented as graphical models and critically how this representation differs from other common graphical representations such as state-transition and block diagrams. We will use this framework to show how one may easily envisage novel models and gain insight into their computational implementation.

Published in:

Signal Processing Magazine, IEEE  (Volume:27 ,  Issue: 6 )