Computer Science ›› 2017, Vol. 44 ›› Issue (Z11): 106-109.doi: 10.11896/j.issn.1002-137X.2017.11A.021

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CSI 300 Index Prediction Research Based on Multi-output Learning

TANG Yan-qin, PAN Zhi-song and ZHANG Yan-yan   

  • Online:2018-12-01 Published:2018-12-01

Abstract: In the stock market,people usually depend on the historical trading data to predict the trend of future.The method of SVM is more common in the stock prediction,but it is complex and time-consuming,and usually predicts the trend of one day.This article adopted regularization method of multi-output learning to predict the trend of many days.We improved the method of multi-task learning and put forward the method based on multi-output learning.Experiments in CSI 300 index show that the mean square error(MSE) of prediction in this method is about 10 times compared to that in the support vector machine (SVM) method,and the time consuming is also reduced nearly three-quarters.

Key words: Multi-output learning,Regression,Stock prediction,Task correlation

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