%0 Journal Article
%T 基于机器学习方法的上证综合指数的预测分析<br>Forecast Analysis of Shanghai Composite Index Based on Machine Learning Method
%A 吴仍康
%J Hans Journal of Data Mining
%P 1-8
%@ 2163-1468
%D 2016
%I Hans Publishing
%R 10.12677/HJDM.2016.61001
%X <div style=\"text-align:justify;\">
	<span style=\"line-height:1.5;\">上证综合指数是广大投资者关注的重要指数。上证综合指数不仅反映了我国股票市场的基本状况，同时对我国经济走向也具有重要的导向作用。对上证综合指数的预测分析以及趋势研判对稳定市场、引导投资者具有重大意义。而股票市场数据是典型的非线性系统，传统统计学预测方法在处理时预测精度较低。本文综合运用R软件并结合目前机器学习领域最新的六种方法——决策树、boosting、bagging、随机森林、支持向量机、神经网络分别对训练集进行训练，得到相应模型。并建立相应的十折交叉验证集计算出每种方法的预测均方误差进行对比。筛选出效果较好的模型，并对预测数据与真实数据进行数据可视化对比。对结果分析可知，随机森林、支持向量机两种机器学习方法拟合效果较好，且精度高。&lt;br/&gt;The Shanghai composite index is an important index that general investors pay close attention to. Shanghai composite index, which not only reflects the basic situation of the stock market in our country, but also takes an important guiding role to our economy. Prediction of Shanghai composite index and trend analysis plays an important role to stabilize market and guide investors. And stock market data are a typical nonlinear system; traditional statistical forecasting methods predict a low accuracy. In this paper, we use R software comprehensively and combine with the latest six kinds of methods in machine learning field, decision tree, boosting, bagging, random forests, support vector machine (SVM), neural network to train the training set, respectively, get the corresponding model. And set up the corresponding ten-fold cross validation to calculate the prediction mean square error of each method for comparison. Select the model with better effect, and make a visualized comparison between prediction data and real data. Analysis shows that the results of random forests, SVM are more fitting, and have high precision.</span>
</div>
%K 上证综合指数，机器学习，随机森林，支持向量机<br>Shanghai Composite Index
%K Machine Learning
%K Random Forests
%K SVM
%U http://www.hanspub.org/journal/PaperInformation.aspx?PaperID=16757