By Topic

Modeling nonlinear 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
$33 $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

1 Author(s)
A. M. Fraser ; Portland State Univ., OR, USA

It is argued that the ubiquity of strange attractors in nature suggests that using nonlinear modeling techniques might improve performance in some signal processing applications. A synthetic data set generated by numerically integrating a simple nonlinear differential equation is described, and the case with which crude nonlinear methods outperform linear methods is illustrated. The synthetic data are fit by linear autoregressive moving average (ARMA) models and three nonlinear methods: piecewise linear, hidden Markov models (HMM) with discrete outputs, and HMMs with continuous autoregressive outputs (ARHMM). Criteria for assessing model performance are discussed, and connections between these criteria and fundamental invariants developed in ergodic theory are noted

Published in:

Acoustics, Speech, and Signal Processing, 1992. ICASSP-92., 1992 IEEE International Conference on  (Volume:5 )

Date of Conference:

23-26 Mar 1992