Stock prediction method, system and device based on affinity propagation and medium

Abstract

The invention relates to a stock prediction method, system and device based on affinity propagation, and a medium, and the method comprises the steps: obtaining the historical transaction data of a plurality of stocks, and processing the historical transaction data of any one stock to obtain a target feature vector set of the stock; directly obtaining target feature vector sets of other remainingstocks according to the target feature vector set of the stock; performing affinity propagation clustering analysis on all historical transaction data according to all the target feature vector sets by adopting an affinity propagation method to obtain a clustering result; selecting a preset number of sample stocks in the cluster to which the to-be-predicted stocks belong according to the clustering result, and obtaining a stock trend prediction model according to the target feature vector set and historical transaction data corresponding to the sample stocks based on a support vector machine training method; and predicting the to-be-predicted stock according to the stock trend prediction model to obtain a prediction result. The invention provided by the invention has the advantages of relatively low operand, relatively short operation time, small occupied memory, high parallelism and high prediction accuracy.

Description

technical field [0001] The present invention relates to the technical field of stock ups and downs trend prediction in a specific time period, in particular to a stock prediction method, system, device and medium based on neighbor propagation. Background technique [0002] The stock market is an important place for the optimal allocation of capital resources, and mastering its changing laws is not only a dream for investors, but also has important practical significance for the research and management of the macro national economy. Since the factors affecting the stock price include internal factors of the enterprise, economic factors, institutional factors and people's psychological factors, etc., and the degree and mode of influence of various factors are different, it is very difficult to accurately predict the stock price. At present, stock price forecasting is mainly to predict the rising and falling trends of stocks. [0003] With the development of Internet technolog...

AI Technical Summary

Problems solved by technology

Commonly used algorithms include: BP neural network algorithm, RNN cyclic neural network algorithm and LSTM neural network algorithm, etc. Among them, BP neural network algorithm has strong nonlinear mapping ability and self-learning ability, but the learning speed is slow and prone to " Overfitting” phenomenon; the RNN cyclic neural network algorithm will not only learn the information at the current moment, but also rely on the previous sequence information, but when the distance increases, the RNN becomes unable to connect relevant information; the neurons in the LSTM neural network algorithm have special The gate structure can learn long-distance dependencies, but it takes up a lot of memory and cannot be processed in parallel

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