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Stock price prediction method based on space-time diagram attention mechanism

A forecasting method and attention technology, applied in forecasting, neural learning methods, instruments, etc., can solve the problems of not considering, low accuracy of price changes, etc., and achieve the effect of improving accuracy

Pending Publication Date: 2020-12-25
SHENZHEN GRADUATE SCHOOL TSINGHUA UNIV +1
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  • Claims
  • Application Information

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Problems solved by technology

At the same time, when predicting the price changes of a single stock or market index based on time-series data, the time-series data of the stock will be regarded as independent data, and the correlation between the stock and other stocks will not be considered, so that the predicted single stock or market Low accuracy of index price changes

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  • Stock price prediction method based on space-time diagram attention mechanism
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  • Stock price prediction method based on space-time diagram attention mechanism

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Embodiment Construction

[0047] This application provides a stock price prediction method based on the attention mechanism of space-time graph. In order to make the purpose, technical solution and effect of this application more clear and definite, the following will further describe this application in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described here are only used to explain the present application, not to limit the present application.

[0048] Those skilled in the art will understand that unless otherwise stated, the singular forms "a", "an", "said" and "the" used herein may also include plural forms. It should be further understood that the word "comprising" used in the specification of the present application refers to the presence of the features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, Integers, steps, operations, elem...

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Abstract

The invention discloses a stock price prediction method based on a space-time diagram attention mechanism, and the method comprises the steps: obtaining stock relation data corresponding to a plurality of target stocks and time sequence data corresponding to each target stock, and determining stock dynamic data corresponding to the plurality of target stocks based on the obtained stock relation data and time sequence data; and based on the stock dynamic data, determining stock prediction data corresponding to each target stock in a plurality of target stocks. According to the invention, when the stock prediction data is predicted, the stock relation data used for reflecting the industry correlation between the stocks is determined besides the time sequence data corresponding to each stockis obtained, and the stock prediction is carried out based on the time sequence data and the stock relation data, so that the accuracy of stock prediction can be improved.

Description

technical field [0001] The present application relates to the technical field of trend prediction, in particular to a stock price prediction method based on a space-time graph attention mechanism. Background technique [0002] Currently, the methods applied to stock prediction generally use time series models (such as ARIMA model) and deep learning models (such as LSTM). These methods generally use time series data including daily price, trading volume, and price-earnings ratio to predict individual stocks. Or the price change of a market index. At the same time, when predicting the price changes of a single stock or market index based on time-series data, the time-series data of the stock will be regarded as independent data, and the correlation between the stock and other stocks will not be considered, so that the predicted single stock or market The accuracy of the price change of the index is low. Contents of the invention [0003] The technical problem to be solved ...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06Q40/04G06N3/04G06N3/08
CPCG06Q10/04G06Q40/04G06N3/08G06N3/048G06N3/045
Inventor 夏树涛鲍际刚孙继丰李佳维刘鑫吉圣亚军朱天磊夏智康
Owner SHENZHEN GRADUATE SCHOOL TSINGHUA UNIV
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