Stock prediction method and device

Abstract

The invention provides a stock prediction method and a device, which relate to the technical field of stock historical data prediction. Determining a fitness function containing undetermined coefficients according to the training sample and the support vector machine model; taking The fish swarm algorithm to compute the preset undetermined coefficient samples to obtain the optimal undetermined coefficient. In accordance with that train sample, the optimal waiting coefficients and the support vector machine model determine the way to predict the data, The undetermined coefficients of the support vector machine model are updated by fish swarm algorithm to avoid the uncertainty of artificial setting parameters, so that even if the amount of data is large, the support vector machine model canbe updated in real-time by the way of update iteration, which is not only suitable for changeable scenes, but also can achieve the technical effect of improving the accuracy of prediction results.

Description

technical field [0001] The invention relates to the technical field of stock historical data forecasting, in particular to a stock forecasting method and device. Background technique [0002] With the increase in the number of listed companies, the number of stock transactions has increased dramatically. For the huge stock data prediction, if the traditional method is still used, it is easy to cause low prediction data accuracy. For example, use the support vector machine model to calculate the optimal solution for the stock data sample. , due to the large amount of data, and because the Gaussian kernel function width and penalty factor of the support vector machine model itself are fixed, it is easy to cause poor robustness of the prediction process and cannot be better applied to variable scenarios, resulting in low accuracy of prediction results. Contents of the invention [0003] In view of this, the purpose of the present invention is to provide a stock forecasting me...

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Problems solved by technology

[0003] In view of this, the object of the present invention is to provide a stock forecasting method and device to solve the problems in the prior art due to the large amount of data, and because the Gaussian kernel function width and penalty factor of the support vector machine model itself are fixed, which is easy to cause The forecasting process has poor robustness and cannot be better applied to variable scenarios, resulting in low accuracy of forecasting results

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