Transaction object prediction method and device

Abstract

The invention discloses a transaction object prediction method and device, and relates to the technical field of computers. One specific embodiment of the method comprises the steps of obtaining a plurality of prediction reference texts of a target transaction object in each period of a plurality of historical transaction periods, and determining a fluctuation tendency factor of each prediction reference text; determining a text tendency index of the target transaction object in the period by utilizing the rising and falling tendency factor; and inputting the price data and the text tendency index of the target transaction object in each period of the plurality of historical transaction periods into a pre-trained prediction model to obtain information of at least one preset dimension of the target transaction object in a future transaction period. According to the embodiment of the invention, the method can improve the prediction accuracy of the transaction object through introducing the features, affecting the change trend of the transaction object, in the prediction reference text into the model for training and prediction and analyzing a large amount of historical data containing the features.

Description

technical field [0001] The present invention relates to the field of computer technology, in particular to a transaction object prediction method and device. Background technique [0002] With the development of big data and artificial intelligence, it is necessary to apply machine learning and other technologies to the rapidly changing financial market (such as the stock market) for information forecasting, so as to overcome the defects of low accuracy and inability to guide practice in existing forecasting methods. [0003] In the existing technology, one-time exponential smoothing method is generally used to process the time series of historical information of transaction objects to achieve forecasting. Due to the limitations of this method and the characteristics that stock information is easily affected by various internal and external factors, when the time series shows a linear trend, There is a large lag in the prediction results, which seriously affects the predicti...

AI Technical Summary

Problems solved by technology

[0002] With the development of big data and artificial intelligence, it is necessary to apply machine learning and other technologies to the ever-changing financial market (such as the stock market) for information forecasting, so as to overcome the defects of low accuracy and inability to guide practice in existing forecasting methods

[0003] In the existing technology, one-time exponential smoothing method is generally used to process the time series of historical information of transaction objects to achieve forecasting. Due to the limitations of this method and the characteristics that stock information is easily affected by various internal and external factors, when the time series shows a linear trend, There is a large lag in the prediction results, which seriously affects the prediction accuracy

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