基于主成分分析的股票指数预测研究

Stock Forecasting Based on Principal Component Analysis

预测中输入变量的选取影响预测的速度和精度,传统方法选取输入变量主观性强,预测效果欠佳。本文使用主成分分析法选取输入变量,计算量小,预测效果更好。以沪市综合指数预测为例进行仿真计算,仿真结果表明了使用主成分分析法选取输入变量的有效性,它明显减少了预测时间,改善了预测精度。

The inputs of forecasting affect the accuracy of the results and computational speed. The traditional method of subjective selection is inefficient in choosing the inputs. The Principal Component Analysis（PCA） method is applied to preprocessing the data sets, eliminating the correlation among the inputs,and simplifying the structure. An example of Shanghai Stock Index is used to prove the efficiency of PCA.