Stock price prediction method, system and medium for fusing text multi-theme information

Abstract

The present invention provides a stock price prediction method, system and medium that integrate text multi-theme information, including: a data preprocessing step: acquiring text data and stock data, preprocessing the acquired text data and stock data, and obtaining the preprocessed Post-data; model training step: according to the obtained pre-processed data, the stock price prediction model is trained to obtain the trained model. The present invention uses a multi-head attention mechanism to map text vectors to different semantic spaces, then extracts topic information, and finally fuses different topic information with different attention weights skillfully. In addition, the present invention also utilizes the encoder-decoder framework to effectively integrate text information and stock price information into a unified framework, and can dynamically adjust the influence of different time nodes on stock price prediction.

Description

technical field [0001] The present invention relates to the technical field of stock price forecasting, in particular to a stock price forecasting method, system and medium for fusing text multi-theme information. Background technique [0002] In recent years, mining massive text information to predict stock prices has achieved good results. However, these methods basically use a fixed-length vector to represent each text, ignoring that each text may contain multiple topics and these different topics may have different effects on stock prices. In order to make full use of the information of different topics in the text, the present invention designs a multi-head attention mechanism to map the text to different semantic spaces. Since there is redundant information in different texts, the pooling operation is used to extract the topic information of different semantic spaces, and finally the different topics are used to The influence of the integration of different topics of ...

AI Technical Summary

Problems solved by technology

Although the topic model is a commonly used method to extract text topics, the topic model separates topic recognition and prediction, which is not conducive to reverse error propagation, and the topic model cannot solve the problem that different topics have different influences on stock price prediction.

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