Integrating piecewise linear representation and Gaussian process classification for stock turning points prediction
LI Feng1, GAO Feng1, KOU Peng2
1. System Engineering Institute, Xi'an Jiaotong University, Xi'an Shaanxi 710049, China;
2. College of Electrical Engineering, Xi'an Jiaotong University, Xi'an Shaanxi 710049, China
Focusing on the prediction issue of the price turning point in stock trading process, a prediction algorithm of stock price turning point, named PLR-GPC, was proposed based on Piecewise Linear Representation (PLR) and Gaussian Process Classification (GPC). The algorithm extracted the turning points of the historical stock price series by PLR, and classified the points with different labels. A prediction model of the stock price turning point was built based on GPC, and it was trained with the turning points extracted by PLR. Eventually, the model could predict whether a new price would be a price turning point, and could explain the result with probability. An experiment on the real stock data was carried out among PLR-GPC, PLR-BPN (PLR-Back Propagation Network), and PLR-WSVM (PLR-Weighted Support Vector Machine). It showed that the PLR-GPC had higher forecast accuracy than the other two algorithms, and its rate of return was higher than PLR-BPN, almost equal to PLR-WSVM. The experimental result proves that the PLR-GPC is effective on stock turning point prediction and it can be applied in the actual stock investment trading.
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2015, Vol. 35 
