Intelligent terminal and stock trend prediction method based LSTM thereof

Abstract

The invention relates to an intelligent terminal and a stock trend prediction method based LSTM thereof. The method comprises steps of acquiring history data of a target stock, carrying out data cleaning and normalization, and dividing the data into a training data set and a test data set according to time; carrying out an offline model training on the training data so as to train multiple neural network models of the LSTM separately; acquiring a prediction value list output by the multiple neural network models of the training data, and comparing the prediction value list with an actual stock trend value to calculate an occupied weight value when the neural network models are used as a combined model; and using the test data of the test data set to estimate evaluation results of the neural network models in the combined model, thereby adjusting the occupied weight value when the neural network models are used as the combined model. According to the invention, in a way of the combined model, problems of quite big errors and quite low practicability of a simple prediction method of a single LSTM model are avoided.

Description

technical field [0001] The present application relates to the field of artificial intelligence technology, in particular to a stock trend prediction method based on LSTM (Long-Short Term Memory, long-short-term memory neural network), and also relates to an intelligent terminal that executes and realizes the method. Background technique [0002] The stock market is a barometer of economic operations. If investors can accurately grasp the changing laws of the stock market, they can not only obtain huge returns, but also avoid investment risks. For government regulation, it can not only formulate reasonable policies in advance to guide the healthy development of the market, but also warn listed companies of risks in advance. Therefore, stock market trend forecasting has always been a key issue related to the national economy and people's livelihood. It is not only the Holy Grail pursued by investors, but also a difficult point in financial market supervision. [0003] Howeve...

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Problems solved by technology

[0010] Based on this, it is necessary to provide an intelligent terminal and its LSTM-based stock trend prediction method for the above-mentioned simple prediction method of the LSTM model with large erro

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