NDLI: Local Prediction of Complex Time Series Based on Support Vector Machine and Differential Evolution Algorithm

Content Provider	IEEE Xplore Digital Library
Author	Jun Wang Jia Zhang Huang-Chang Xu
Copyright Year	2009
Abstract	Prediction on complex time series has received much attention during the last decades. Global model is the main tool for time series predicting during the last decades, but it suffers low prediction efficiency, low prediction accuracy and high computation complexity for model training and updating. In recent years, local model for time series prediction draws widely attention for its more accuracy prediction ability, lower complexity of models and lower computation complexity of modeling. In this paper, a new scheme for time series prediction is proposed, in which nearest neighbor searching technique is used to searching the top k most similar data samples of the data point waiting for prediction, and then support vector regressing model is constructed with the top k most similar data point with differential evolution algorithm to do SVR training and parameter optimization. This proposed method is applied to three real world complex time series. The method provides relatively better prediction performance in comparison with the others.
Starting Page	425
Ending Page	428
File Size	389859
Page Count	4
File Format	PDF
ISBN	9780769538655
DOI	10.1109/ISCID.2009.252
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-12-12
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines support vector regression differential evolution algorithm nearest neighbor searching Local prediction
Content Type	Text
Resource Type	Article

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