Data prediction method of combined LSTM model based on two-dimensional data stream

Abstract

The invention provides a stock prediction method of a combined LSTM model based on a two-dimensional data stream. The method comprises the following steps of converting one-dimensional stock sequencedata flow into a two-dimensional data flow containing time context information; carrying out feature extraction on the two-dimensional data flow by using a convolutional neural network; then respectively establishing an LSTM prediction model of a single stock price, a stock market index and an involved industry index on the two-dimensional data stream; and predicting a single stock price, a stockmarket index and an involved industry index by using the respective LSTM model, then establishing a relationship model between the single stock and the stock market index and between the single stockand the involved industry index respectively, and forming a combined prediction model on the basis of the relationship model. The stock prediction method of the combined LSTM model based on the two-dimensional data flow has good robustness.

Description

technical field [0001] The invention relates to the field of stock price forecasting, in particular to a stock forecasting method based on a combined LSTM model of two-dimensional data streams. Background technique [0002] In recent years, with the rapid development of artificial intelligence technology and big data technology, the demand in the financial market has become increasingly strong, and stock forecasting is one of the areas with the strongest demand. Traditional stock prediction methods include: support vector machine, random forest, adboost, logistic regression, etc. With the continuous improvement of the performance of deep learning methods in various fields, new tools such as RNN (Recurrent Neural Network), GRU (Gated Recurrent Unit), and LSTM (Long-Short Term Memory, long-term short-term memory neural network) are gradually favored. . [0003] The stock market is a multi-variable nonlinear dynamic system, and its changing rules are affected by many factors,...

AI Technical Summary

Problems solved by technology

Whether it is a traditional simple model based on the price of the stock itself or a complex model based on various news, the prediction of the stock lacks robustness.

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