中文版 | English
Title

OCET: One-dimensional Convolution Embedding Transformer for Stock Trend Prediction.

Author
Corresponding AuthorGuiying Li
Publication Years
2022-12
Conference Name
the 17th International Conference on Bio-inspired Computing: Theories and Applications (BIC-TA 2022)
Source Title
Conference Date
2022-12-16
Conference Place
武汉
Abstract

Due to the strong data fitting ability of deep learning, the use of deep learning for quantitative trading has gradually sprung up in recent years. As a classical problem of quantitative trading, Stock Trend Prediction (STP) mainly predicts the movement of stock price in the future through the historical price information to better guide quantitative trading. In recent years, some deep learning work has made great progress in STP by effectively grasping long-term timing information. However, as a kind of real-time series data, short-term timing information is also very important, because stock trading is high-frequency and price fluctuates violently. And with the popularity of Transformer, there is a lack of an effective combination of feature extraction and Transformer in STP tasks. To make better use of short term information, we propose One-dimensional Convolution Embedding (OCE). Simultaneously, we introduce effective feature extraction with Transformer into STP problem to extract feature information and capture long-term timing information. By combining OCE and Transformer organically, we propose a noval STP prediction model, One-dimensional Convolution Embedding Transformer (OCET), to capture long-term and short-term time series information. Finally, OCET achieves a highest accuracy up to 0.927 in public benchmark FI-2010 When reasoning speed is twice that of SOTA models and a highest accuracy of 0.426 in HKGSAS-2020. Empirical results on these two datasets show that our OCET is significantly superior than other algorithms in STP tasks. Code are available at https://github.com/langgege-cqu/OCET.

Keywords
SUSTech Authorship
First
Language
English
Indexed By
Data Source
人工提交
Document TypeConference paper
Identifierhttp://kc.sustech.edu.cn/handle/2SGJ60CL/523893
DepartmentDepartment of Statistics and Data Science
工学院_计算机科学与工程系
工学院_斯发基斯可信自主研究院
Affiliation
1.Department of Statistics and Data Science, Southern University of Science and Technology, Shenzhen 518055, Chin
2.Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation, Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, Chin
3.Research Institute of Trustworthy Autonomous Systems, Southern University of Science and Technology, Shenzhen 518055, China
4.Shenzhen Securities Information Co., Ltd., Shenzhen, China
First Author AffilicationDepartment of Statistics and Data Science;  Department of Computer Science and Engineering
Corresponding Author AffilicationDepartment of Statistics and Data Science;  Research Institute of Trustworthy Autonomous Systems
First Author's First AffilicationDepartment of Statistics and Data Science
Recommended Citation
GB/T 7714
Peng Yang,Lang Fu,Jian Zhang,et al. OCET: One-dimensional Convolution Embedding Transformer for Stock Trend Prediction.[C],2022.
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