基于时间序列的支持向量机在股票预测中的应用

由于股票预测是不确定、非线性、非平稳的时间序列问题，传统的方法往往难以取得满意的预测效果。本文提出一种基于时间序列的支持向量机（SVM）股票预测方法。利用沙河股份的股票数据，建立股票收盘价回归预测模型，该模型克服了传统时间序列预测模型仅局限于线性系统的情况。实验结果表明，该方法比神经网络方法以及时间序列方法的预测精度更高，可以很好的应用某些非线性时间序列的预测中。

Application of Support Vector Machine Based on Time Sequence in Stock Forecasting

Because stock forecasting is a uncertain,nonlinear and nonstationary time series problem,it is difficult to achieve a satisfying prediction effect by traditional methods.This paper presents a novel stock forecasting method in which an improved Support Vector Machine(SVM) algorithm based on time sequence. Using Shahe' s stock data,a prediction model of the closing price regression is established.The model abstains from the default of traditional time series prediction model that only can be used in linear system.The experiment results are also compared with Neural networks and time sequence methods,which indicate that the SVM strategy can improve precision and therefore this prediction model can be effectively used in some nonlinear time series forecasting.