By Topic

Prediction Method of Time Series Data Stream Based on Wavelet Transform and Least Squares Support Vector Machine

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)
Yinghui Kong ; Sch. of Electr. & Electron. Eng., North China Electr. Power Univ., Baoding ; Yancui Shi ; Jinsha Yuan

Time series data stream is widely concerned in industry engineering, finance, economy, traffic and many other fields, and data stream prediction is the important work. An efficient method for prediction of time series data stream using wavelet transform and least squares support vector machine (LS-SVM) is presented, which can provide high accuracy and cost less time. Sliding window model is used to follow the data changing, incremental algorithms for LS-SVM is used to save time. Simulation experiment using real power load dataset show the effectiveness of proposed method.

Published in:

Natural Computation, 2008. ICNC '08. Fourth International Conference on  (Volume:2 )

Date of Conference:

18-20 Oct. 2008