Inference Algorithm for Stock Market Trend Disturbance Based on Hierarchical Dynamic Bayesian Network

被引：0

作者：

Yao H. ^{[1
]}

Jia H. ^{[1
]}

Yang J. ^{[1
]}

Yu K. ^{[1
]}

机构：

[1] School of Computer Science and Information Engineering, He-fei University of Technology, Hefei

来源：

Moshi Shibie yu Rengong Zhineng/Pattern Recognition and Artificial Intelligence | 2022年 / 35卷 / 04期

基金：

中国国家自然科学基金;

关键词：

Dynamic Bayesian Network; Junction Tree; Markov Blanket; Sensitivity Analysis; Stock Market Trend Forecast;

D O I：

10.16451/j.cnki.issn1003-6059.202204006

中图分类号：

学科分类号：

摘要：

The current research mainly focuses on the forecasting models generated by the learning of historical transaction data. Due to the dynamic variation of the factors affecting the market, the forecasting effect of the trained model in practical applications is much worse than the expected. To solve the problem of weak adaptability of the existing forecasting models, a disturbance inference algorithm based on hierarchical dynamic Bayesian network(DA-NEC) is proposed to predict stock market trends in real time. Firstly, for the moving average data with high stability, the energy of the moving average is extracted through the Markov blanket fusion of the moving average features, and the quantitative characteristics of the moving average are generated. Since the structural relationship among multiple moving averages possesses strong anti-noise ability and stability, the hierarchical dynamic Bayesian network is employed to model the internal structure of a single moving average and the structural relationship among multiple moving averages. Then, the state of multiple nodes in the top-level network is disturbed, and the state changes of the nodes are calculated in real time through dynamic sensitivity analysis. In the end, based on the results of sensitive analysis, the junction tree is applied for dynamic inference on the stock market trend. Experimental results on actual data show the effectiveness of the proposed algorithm. © 2022, Science Press. All right reserved.

引用

页码：363 / 373

页数：10

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