  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.
