NDLI: Evolving Neural Network with Dynamic Time Warping and Piecewise Linear Representation System for Stock Trading Decision Making

Content Provider	IEEE Xplore Digital Library
Author	Pei-Chann Chang Chin-Yuan Fan Chen-Hao Liu Yen-Wen Wang Jyun-Jie Lin
Copyright Year	2009
Abstract	Stock turning points detection is a very interesting subject arising in numerous financial and economic planning problems. In this paper, Evolving Neural Network model with Dynamic Time warping Piecewise Linear Representation system for stock turning points detection is presented. The piecewise linear representation method is able to generate numerous stocks turning points from the historic data base, then Evolving Neural Network model will be applied to train the pattern and retrieve similar stock price patterns from historic data for training the system. These turning points represent short-term trading signals for selling or buying stocks from the market. it is applied to forecast the future turning points from the set of test data. Experimental results demonstrate that the hybrid system integrating DPLR and evolving neural networks can make a significant and constant amount of profit when compared with other approaches using stock data.
Starting Page	303
Ending Page	307
File Size	472968
Page Count	5
File Format	PDF
ISBN	9780769535074
DOI	10.1109/CSIE.2009.36
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-03-31
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Piecewise linear techniques Decision making Artificial neural networks Evolving neural network Industrial engineering Turning Information management Stock turning points Dynamics Time Warping system Engineering management Neural networks Economic forecasting PLR method Stock markets
Content Type	Text
Resource Type	Article

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