一种基于流特征模式的股市跟踪预测算法

Stock Market Tracking Prediction Algorithm Based on Stream Feature Model

由于股市波动的突发性、多变性,且时序数据呈非正态分布,传统的时序预测模型难以有效预测股市。提出了一种基于流特征模式的股市跟踪预测算法(SFM-PG),该算法根据股票之间的相关性构建贝叶斯网络,选取目标股票的马尔科夫毯作为其同辈群体,然后基于同辈群体之间的接近度,给出一种窗口跟踪式预测模型,其通过对同辈群体权重的动态更新进行跟踪式预测,以减少股票数据分布非正态性对预测的影响;进而,使用滑动窗口提取时序数据中的特征并形成流特征,通过与模式知识库的匹配提取流特征模式,并利用与流特征模式对应的知识调整预测结果,以减少由于突变所引入的预测误差。最后,在上证股票板块网络上的实验结果显示了算法的实用性和有效性。

Because stock market volatility is of mutability and variability, and the distribution of the time series data does not follow the normal distribution, the traditional time series forecast algorithms are difficult to accurate predic- tion. The stock market tracking prediction algorithm based on Stream Feature Model was proposed （SFM-PG）. It is builds the Bayesian networks based on correlation between stocks, selects the Markov Blanket of the target stock as its peer group, and gives a windows tracking prediction model based on the proximity between peer group, through dynami- clly updating the weight of peer group to tracking prediction, effective avoids the influence of the non-normal distribu- tion of time series data on prediction. And then, using the sliding window to extract the feature of the time series data to formation stream feature,and extracting the stream feature model by matching with knowledge base of base, using the knowledge of stream feature model to adjust the predicted results, in order to reduce the prediction error introduced by mutability. Finally, the practicability and effectiveness are showed in the experiment on the network of plate of the Shanghai stock.