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

Abstract 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 increasing complexity networks due to growing size datasets. To enable effective comparison, new dataset is created using 15 input parameters from original Chinese stock market Kaggle dataset. Additionally, five DGRU-based models are developed, including chaotic MPA (CMPA) nonlinear (NMPA), as well best Levy-based variants, such dynamic Levy flight chimp optimization (DLFCHOA) Levy-base gray wolf (LGWO). results indicate that most accurate forecasting among tested algorithms DGRU-IMPA, followed DGRU-NMPA, DGRU-LGWO, DGRU-DLFCHOA, DGRU-CMPA, traditional DGRU. findings highlight potential proposed improve accuracy FAIS, leading enhanced decision-making management.