A feature-enhanced long short-term memory network combined with residual-driven ν support vector regression for financial market prediction. (February 2023)

• Record Type: Journal Article
• Authors: Zhang, Yameng; Song, Yan; Wei, Guoliang
• Is Part Of: Engineering applications of artificial intelligence. Volume 118(2023)
• Publication Date: 2023-02
• Subjects: LSTM network -- Residual-driven -- Support vector regression -- Feature extraction -- Financial market forecasting; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2022.105663
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: In recent years, the long short-term memory (LSTM) network has gained special attention in the investigation of financial market forecasting since it has a good ability to mine crucial information from time series data via network learning. However, most existing LSTM networks cannot perform well on the small number of samples and usually have a weak feature extraction and inadequate use of information. To address the above issues, a novel LSTM network, namely feature-enhanced LSTM network combined with residual-driven ν support vector regression, is put forward. Such a proposed LSTM network has the following two whelming merits: (1) the convolution layers are utilized to extract crucial profitable latent features and then the LSTM is applied to gain the rough prediction by using both long-term and short-term information; (2) a residual-driven ν support vector regression ( ν SVR) model is developed to make an promotion over the rough prediction by taking full consideration of historical information. Finally, extensive experiments in real-world datasets demonstrate the desirable results of the proposed method as opposed to other baseline models. Highlights: A feature-enhanced LSTM network combined with residual-driven ν SVR is proposed. The feature-enhanced LSTM model is used to extract features and get a rough result. The residual-driven ν SVR model is developed to improve the rough prediction. Experiments on nine empirical datasets demonstrate the effectiveness of our model.