# A Fast Evidential Approach for Stock Forecasting

arXiv: https://arxiv.org/abs/2104.05204 · HTML: https://mlsanity.com/paper/2104.05204/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 72 references that are in the map.
- First posted on arXiv 12 Apr 2021.

## Builds on (most cited first, 3 of 3)

- [Multi-channel EEG recordings during a sustained-attention driving task](https://mlsanity.com/paper/1809.06534/) (2018)
- [A generalized intelligent quality-based approach for fusing multi-source information](https://mlsanity.com/paper/1910.06150/) (2019)
- [A correlation coefficient of belief functions](https://mlsanity.com/paper/1612.05497/) (2016)

## Built on by (most cited first, 2 of 2)

- [Construction Cost Index Forecasting: A Multi-feature Fusion Approach](https://mlsanity.com/paper/2108.10155/) (2021)
- [Evaluating importance of nodes in complex networks with local volume information dimension](https://mlsanity.com/paper/2111.13585/) (2021)

## Abstract (arXiv)

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.