基于状态演化的股市变化趋势可视化预测方法

A Visual Stock Market Trend Forecasting Method Based on State Evolution

股票市场影响因素众多且关系复杂，对其趋势进行预测十分困难．文中借鉴空间重构技术和可视化数据分析技术表达复杂系统规律和特征的优势，提出了以股市分钟级交易信息为基础的股票市场趋势预测的图形化方法．以沪深两市作为研究对象，选取了开盘价等8个指标作为表征股市系统特征的状态变量，并按一定的周期和递进步长将时序数据绘制成一组股票状态演化图．在对股票状态演化图图像特征与股市趋势变化进行分析的基础上，以图形质心和面积变化率作为股市趋势发生逆转的判断依据，对沪深两地2010年1月～2011年9月股票市场进行了趋势预测实验．结果表明，与其他方法相比，文中方法准确率高、误报率和漏报率低．

Stock market trend forecasting is difficult because the stock market is affected by many factors which influence each other. Inspired by phase space reconstruction and visual data mining technique, this paper proposed a visual method to predict stock trend changes based on minute-level data. Research on Shanghai and Shenzhen stock market, eight indexes are selected as stock state variables, and a series of stock state evolution graphs are drawn in terms of cycle and step. After analyzing the correlation between the features of stock state evolution graph and stock price trend changes, variance ratios of centroid and area between two adjacent graphs are taken as criteria to catch stock trend change signals. The experimental results which sourced from 1-minute and 5-minute data of Shanghai composite index and Shenzhen component index from January 2010 to September 2011 showed stock state evolution graphs has higher accuracy, lower rate of false positives and false negatives than other stock forecast method.