Research on the Forecast of the Combined GRU Model Based on the Optimized Bat Algorithm in the Bank Transaction Volume

Abstract

The accurate prediction of the transaction volume of the core accounting system is of great significance to the stable operation of commercial banks. After fully investigating the transaction volume history of the core accounting system and discovering the unique time-series attributes of the data, this study proposes a transaction volume prediction model of the core accounting system of commercial banks based on the improved bat algorithm and the optimized gating loop unit neural network. The chaos algorithm, reverse learning, evolution, and search mechanisms are implemented to improve the search efficiency of solving the global optimization problem, to overcome the shortcomings of the original bat algorithm, such as early maturation and easily falling into the trap of a local optimal solution, and to enhance the algorithm's optimization ability and precision. Moreover, the bat algorithm's optimization ability is fully utilized, and the optimal parameters of the GRU model, such as network layers and neural units, are determined. Finally, the effectiveness of the combination model is evaluated using historical transaction data from the core accounting system stored in a bank data warehouse. The experimental results show that the improved combined GRU model performs well in mean squared error, root mean squared error, and mean absolute error, which is superior to the original GRU model and the traditional time-series forecasting ARMA model. The proposed combined model can be effectively applied to the transaction volume prediction of the core accounting system of commercial banks.