建構動態時間校正結合線段切割法於股價買賣點之預測

林俊宇

doi:10.6838/YZU.2008.00120

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建構動態時間校正結合線段切割法於股價買賣點之預測

Using Dynamic Time Warping and Piecewise Linear Representation Method on stock trading points’ prediction

林俊宇 , Masters　　Advisor：張百棧　　

繁體中文 DOI： 10.6838/YZU.2008.00120

線段切割法；倒傳遞類神經網路；動態時間校正；基因演算法； Piecewise Linear Representation (PLR) ； Back-Propagation Neural Network (BPN) ； Dynamic Time warping system (DTW) ； Genetic Algorithms (GA)

Abstract │ Reference (59) │ Times Cited (4) │ Altmetrics

Abstract 〈TOP〉

Parallel Abstract 〈TOP〉

The stock market has become the main outlet for investment recently in Taiwan. The futures indicator, investment foundations, foreign capitals are diverse choices for investors. How to establish an efficient decision support system on stock market prediction is pretty difficult but it’s necessary. By adopting a good forecasting system, investors may earn more money form the stock market. This research suppose a piecewise linear representation method with a Dynamic Time Warping window for stock turning points detection. The piecewise linear representation method is able to generate numerous local stock turning points from the historic data, then the Dynamic Time Warping window will be applied to 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. A Back-Propagation neural network (B.P.N) is further applied to learn the connection weights from these historic turning points and afterwards it is applied to forecast the future turning points from the set of test data. Experimental results demonstrate that the system integrating PLR and neural networks can make a significant amount of profit when compared with other approaches using stock data such as Taiwan Stock Exchange Market, Google, Lockheed Martin Corporation, Delta Air Lines , and Caterpillar Inc., etc. , to be research object and compute actual return on investment by utilizing the prediction model, DTW-PLR. This research result can make a significant amount of profit when compared with other approaches using stock data to predict better commerce occasions (trading points) than experts’ judgment from technical indexes or other prediction models by experimental results.

Reference ( 59 ) 〈TOP〉

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7. Depei B. and Zehong Y., “Intelligent stock trading system by turning point confirming and probabilistic reasoning,” Expert Systems eith Applications 34, pp.620-627, 2008.
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Times Cited (4) 〈TOP〉

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