A Fast Evidential Approach for Stock Forecasting

TL;DR

This paper introduces a fast evidential method based on evidence theory and Dempster's rule to improve stock price forecasting accuracy while maintaining high efficiency, addressing limitations of traditional time series models.

Contribution

It proposes a novel evidential confidence function for time series points and applies Dempster's rule to fuse growth rates, enhancing stock prediction accuracy and speed.

Findings

Improved forecasting accuracy over traditional methods.

Low time complexity enables fast processing.

Effective handling of uncertainty in stock prediction.

Abstract

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.