Notification:
We are currently experiencing intermittent issues impacting performance. We apologize for the inconvenience.
By Topic

A review of some main improved models for neural network forecasting on 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

3 Author(s)
Ming-liang Chai ; Electron. Inf. & Control Eng. Coll., Beijing Polytech. Univ., China ; Su Song ; Ning-ning Li

Time series forecasting is one of the important problems in the time series analysis. As one of the most powerful analysis tools for time series forecasting, neural network (NN) has been receiving considerable attention since many years ago and a large number of improvements of NN-based forecasting on time series have appeared in the relevant literature. This paper reviews the structure improvement of NN and the main combination of NN and other pop technologies in the improvement of algorithms.

Published in:

Intelligent Vehicles Symposium, 2005. Proceedings. IEEE

Date of Conference:

6-8 June 2005