Evolving deep gated recurrent unit using improved marine predator algorithm for profit prediction based on financial accounting information system

Complex & Intelligent Systems - Trang 1-17 - 2023
Xue Li1, Mohammad Khishe2, Leren Qian3
1School of Management and Economics, North China University of Water Resources and Electric Power, Zhengzhou, China
2Department of Electrical Engineering, Imam Khomeini Marine Science University, Nowshahr, Iran
3School of Computing and Augmented Intelligence, Arizona State University, Tempe, USA

Tóm tắt

This research proposes a hybrid improved marine predator algorithm (IMPA) and deep gated recurrent unit (DGRU) model for profit prediction in financial accounting information systems (FAIS). The study addresses the challenge of real-time processing performance caused by the increasing complexity of hybrid networks due to the growing size of datasets. To enable effective comparison, a new dataset is created using 15 input parameters from the original Chinese stock market Kaggle dataset. Additionally, five DGRU-based models are developed, including chaotic MPA (CMPA) and the nonlinear MPA (NMPA), as well as the best Levy-based variants, such as the dynamic Levy flight chimp optimization algorithm (DLFCHOA) and the Levy-base gray wolf optimization algorithm (LGWO). The results indicate that the most accurate model for profit forecasting among the tested algorithms is DGRU-IMPA, followed by DGRU-NMPA, DGRU-LGWO, DGRU-DLFCHOA, DGRU-CMPA, and traditional DGRU. The findings highlight the potential of the proposed hybrid model to improve profit prediction accuracy in FAIS, leading to enhanced decision-making and financial management.

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