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.
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2022, Vol. 35 
