Title: Application research on variation analysis model for exchange rate based lasso-BP

Authors: Wei Xue; Peng Liu; Zhuan Xin; Jing Chen

Addresses: School of Saxo Fintech, Geely University of China, Chengdu, 610095, China ' Saxo School of Financial Technology, Geely University, Chengdu, 641423, China ' School of Business, Geely University of China, Chengdu, 641423, China ' School of Saxo Fintech, Geely University of China, Shanghai, 200120, China

Abstract: Aiming at the problem of low accuracy of exchange rate forecast in foreign exchange market data analysis, an exchange rate prediction model based on Lasso-IGWO-BP was proposed. Firstly, Lasso regression model is used as feature screening method of exchange rate data. Then, BP neural network is taken as the basic prediction method, and IGWO algorithm is introduced to optimise the parameters of BP neural network. Finally, the extracted features are input into the IGWO-BP model for classification prediction, thus achieving better exchange rate prediction effect. Experimental results show that compared with the Attention-LSTM, PSO-ELM and FWA-GA prediction models, the Lasso-IGWO-BP model constructed in this paper has significantly improved prediction accuracy. Therefore, exchange rate prediction model constructed can provide more reliable exchange rate data for foreign exchange market, which has certain feasibility.

Keywords: exchange rate analysis; data prediction; Lasso; BP neural network; GWO; feature selection; regression analysis; input variables; position update.

DOI: 10.1504/IJCSM.2025.149633

International Journal of Computing Science and Mathematics, 2025 Vol.22 No.1, pp.32 - 49

Received: 28 Nov 2024
Accepted: 05 Jul 2025
Published online: 07 Nov 2025 *

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