Research Area: Machine Learning
In order to mitigate investments, stock price forecasting has attracted more attention in recent years. Aiming at the discreteness, non-normality, high-noise in high-frequency data, a support vector machine regression (SVR) algorithm is introduced in this paper. However, the characteristics in different periods of the same stock, or the same periods of different stocks are significantly different. So, SVR with fixed parameters is difficult to satisfy with the constantly changing data flow. To tackle this problem, an adaptive SVR was proposed for stock data at three different time scales, including daily data, 30-min data, and 5-min data. Experiments show that the improved SVR with dynamic optimization of learning parameters by particle swarm optimization can get a better result than compared methods including SVR and back-propagation neural network.
Keywords:
Support vector machine regression (SVR)
Stock Price Forecasting
Machine Learning
Deep Learning
Author(s) Name: Yanhui Guo; Siming Han; Chuanhe Shen; Ying Li; Xijie Yin; Yu Bai
Journal name: IEEE Access
Conferrence name:
Publisher name: IEEE
DOI: 10.1109/ACCESS.2018.2806180
Volume Information: Volume: 6, Page(s): 11397 - 11404
