Abstract 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.
Fund:National Key R&D Program of China(No.2020AA A0106100), General Program of National Natural Science Foundation of China(No.61876206,62176082)
Corresponding Authors:
YAO Hongliang, Ph.D., associate professor. His research interests include machine learning and data mining.
About author:: JIA Hongyu, master student. His research interests include machine learning and data mining. YANG Jing, Ph.D., associate professor. Her research interests include artificial intelligence and data mining. YU Kui, Ph.D., professor. His research interests include machine learning and causal discovery.
YAO Hongliang,JIA Hongyu,YANG Jing等. Inference Algorithm for Stock Market Trend Disturbance Based on Hierarchical Dynamic Bayesian Network[J]. Pattern Recognition and Artificial Intelligence, 2022, 35(4): 363-373.
[1] CHANG P C, WU J L. The Weighted Support Vector Machines for the Stock Turning Point Prediction // Proc of the 14th International Conference on Intelligent Systems Design and Applications. Wa-shington, USA: IEEE, 2014: 205-210. [2] ZHANG D H, LOU S. The Application Research of Neural Network and BP Algorithm in Stock Price Pattern Classification and Prediction. Future Generation Computer Systems, 2021, 115: 872-879. [3] DE OLIVEIRA F A, NOBRE C N, ZÁRATE L E. Applying Artificial Neural Networks to Prediction of Stock Price and Improvement of the Directional Prediction Index-Case Study of PETR4, Petrobras, Brazil. Expert Systems with Applications, 2013, 40(18): 7596-7606. [4] ZHAO Z Y, RAO R N, TU S X, et al. Time-Weighted LSTM Mo-del with Redefined Labeling for Stock Trend Prediction // Proc of the 29th IEEE International Conference on Tools with Artificial Intelligence. Washington, USA: IEEE, 2017: 1210-1217. [5] QIN X Y, PENG Q K. Stock Turning Point Recognition Using Multiple Model Algorithm with Multiple Types of Features // Proc of the 10th World Congress on Intelligent Control and Automation. Wa-shington, USA: IEEE, 2012: 4020-4025. [6] CHANDRIKA P V, VISALAKSHMI K, SRINIVASAN K S. Application of Hidden Markov Models in Stock Trading // Proc of the 6th International Conference on Advanced Computing and Communication Systems. Washington, USA: IEEE, 2020: 1144-1147. [7] WEN M, LI P, ZHANG L F, et al. Stock Market Trend Prediction Using High-Order Information of Time Series. IEEE Access, 2019, 7: 28299-28308. [8] HOOI B, LIU S H, SMAILAGIC A, et al. BeatLex: Summarizing and Forecasting Time Series with Patterns // Proc of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Berlin, Germany: Springer, 2017: 3-19. [9] CHEN Y H, CHANG C H, KUO S Y, et al. A Dynamic Stock Trading System Using GQTS and Moving Average in the U.S. Stock Market // Proc of the IEEE International Conference on Systems, Man, and Cybernetics. Washington, USA: IEEE, 2020: 848-853. [10] DU X P, ZHU F. A Novel Principal Components Analysis (PCA) Method for Energy Absorbing Structural Design Enhanced by Data Mining. Advances in Engineering Software, 2019, 127: 17-27. [11] FENG Y, WANG H X, YANG H, et al. Time-Continuous Energy-Conservation Neural Network for Structural Dynamics Analysis. Neurocomputing, 2021, 456: 450-460. [12] LEWIS A D, GROTH K M. A Dynamic Bayesian Network Structure for Joint Diagnostics and Prognostics of Complex Engineering Systems. Algorithms, 2020, 13(3): 64.DOI: 10.3390/a13030064 [13] LI Z, YANG J J, TAN S H. Systematically Discovering Depen-dence Structure of Global Stock Markets Using Dynamic Bayesian Network. Journal of Computational Information Systems, 2013, 9(18): 7215-7226. [14] WANG S C, YANG C, LENG C P. Multi-hierarchical Dynamic Bayesian Network Classifier for Operational Risk Early-Warning // Proc of the International Conference on Computer Application and System Modeling. Washington, USA: IEEE, 2010, VIII: 169-173. [15] CHEN F, WANG W. Activity Recognition through Multi-scale Dynamic Bayesian Network // Proc of the 16th International Confe-rence on Virtual Systems and Multimedia. Washington, USA: IEEE, 2010: 34-41. [16] AMIRI S, MAHJOUB M A. HMDHBN: Hidden Markov Inducing a Dynamic Hierarchical Bayesian Network for Tumor Growth Prediction // Proc of the International Conference on Computer Analysis of Images and Patterns. Berlin, Germany: Springer, 2019: 3-14. [17] LASKEY K B. Sensitivity Analysis for Probability Assessments in Bayesian Networks. IEEE Transactions on Systems, Man, and Cybernetics, 1995, 25(6): 901-909. [18] MITROPHANOV A Y, LOMSADZE A, BORODOVSKY M. Sensitivity of Hidden Markov Models. Journal of Applied Probability, 2005, 42(3): 632-642. [19] LOW S T, MOHAMAD M S, OMATU S, et al. Inferring Gene Regulatory Networks from Perturbed Gene Expression Data Using a Dynamic Bayesian Network with a Markov Chain Monte Carlo Algorithm // Proc of the IEEE International Conference on Granular Computing. Washington, USA: IEEE, 2014: 179-184. [20] 姚宏亮,张一鸣,李俊照,等.动态贝叶斯网络的灵敏性分析研究.计算机研究与发展, 2014, 51(3): 536-547. (YAO H L, ZHANG Y M, LI J Z, et al. Research on Sensitivity Analysis of Dynamic Bayesian Networks. Journal of Computer Research and Development, 2014, 51(3): 536-547.) [21] CHAI Y T, LIU H. Reliability Analysis of Conventional Island Water Supply System // Proc of the 4th International Conference on Mechanical, Control and Computer Engineering. Washington, USA: IEEE, 2019: 280-283. [22] RAM R, CHETTY M. A Markov-Blanket-Based Model for Gene Regulatory Network Inference. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2011, 8(2): 353-367. [23] COOPER G F, HERSKOVITS E. A Bayesian Method for the Induction of Probabilistic Networks from Data. Machine Learning, 1992, 9: 309-347. [24] MOON T K. The Expectation-Maximization Algorithm. IEEE Signal Processing Magazine, 1996, 13(6): 47-60. [25] KOIVISTO M, SOOD K. Exact Bayesian Structure Discovery in Bayesian Networks. Journal of Machine Learning Research, 2004, 5: 549-573. [26] COUPÉ V M H, VAN DER GAAG L C. Properties of Sensitivity Analysis of Bayesian Belief Networks. Annals of Mathematics and Artificial Intelligence, 2002, 36: 323-356. [27] EBERHARDT F, GLYMOUR C, SCHEINES R. On the Number of Experiments Sufficient and in the Worst Case Necessary to Identify All Causal Relations Among N Variables[C/OL]. [2021-09-20]. https://arxiv.org/ftp/arxiv/papers/1207/1207.1389.pdf. [28] SELVIN S, VINAYAKUMAR R, GOPALAKRISHNAN E A, et al. Stock Price Prediction Using LSTM, RNN and CNN-Sliding Window Model // Proc of the International Conference on Advances in Computing, Communications and Informatics. Washington, USA: IEEE, 2017: 1643-1647. [29] ZHU D D, CUI Y. Understanding Random Guessing Line in ROC Curve // Proc of the 2nd International Conference on Image, Vision and Computing. Washington, USA: IEEE, 2017: 1156-1159.
2022, Vol. 35 
