In the framework of evidence theory, data fusion combines the confidence functions of multiple different information sources to obtain a combined confidence function . Stock price prediction is the focus of economics. Stock price forecasts can provide reference data. The Dempster combination rule is a classic method of fusing different information. By using the Dempster combination rule and confidence function based on the entire time series fused at each time pointand future time points, and the preliminary forecast value obtainedthrough the time relationship, the accurate forecast value can be restored. This article will introduce the prediction method of evidence theory . This method has good running performance, can make a rapid response on a large amount of stock price data, and has far-reaching significance .
Within the framework of evidence theory, the confidence functions of different information can be combined into a combined confidence function to solve uncertain problems. The Dempster combination rule is a classic method of fusing different information. This paper proposes a similar confidence function for the time point in the time series. The Dempster combination rule can be used to fuse the growth rate of the last time point, and finally a relatively accurate forecast data can be obtained. Stock price forecasting is a concern of economics. The stock price data is large in volume, and more accurate forecasts are required at the same time. The classic methods of time series, such as ARIMA, cannot balance forecasting efficiency and forecasting accuracy at the same time. In this paper, the fusion method of evidence theory is applied to stock price prediction. Evidence theory deals with the uncertainty of stock price prediction and improves the accuracy of prediction. At the same time, the fusion method of evidence theory has low time complexity and fast prediction processing speed .
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This paper proposes a similar confidence function for the time point in the time series. The
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and confidence function based on the entire time series fused at each time pointand future time points, and the preliminary forecast value obtainedthrough the time relationship, the accurate forecast value can be restored. This article will introduce the prediction
can be used to fuse the growth rate of the last time point, and finally a relatively accurate forecast data can be obtained. Stock price forecasting is a concern of economics. The stock price data is large in volume, and more accurate forecasts are required at the same time. The classic methods of time series, such as ARIMA, cannot balance forecasting efficiency and forecasting accuracy at the same time. In this paper, the fusion
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. This method has good running performance, can make a rapid response on a large amount
is applied to stock price prediction. Evidence theory deals with the uncertainty
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data, and has far-reaching significance
prediction and improves the accuracy of prediction. At the same time, the fusion method of evidence theory has low time complexity and fast prediction processing speed