ORIGINAL RESEARCH article
Front. Artif. Intell.
Sec. AI in Finance
Volume 8 - 2025 | doi: 10.3389/frai.2025.1599799
CMDMamba: Dual-Layer Mamba Architecture with Dual Convolutional Feed-Forward Networks for Efficient Financial Time Series Forecasting
Provisionally accepted
- 1Southwest Jiaotong University, Chengdu, China
- 2Network Security Research Center, Guangxi Police College, Nanning, Guangxi Zhuang Region, China
- 3School of Information Technology, Guangxi Police College, Nanning, China
- 4Institute of Software, Chinese Academy of Sciences, Beijing, China
- 5School of Public Administration, Guangxi Police College, Nanning, China
- 6School of Business Administration, Guangxi Vocational \& Technical Institute of Industry, Nanning, China
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Transformer models have demonstrated remarkable performance in financial time series forecasting. However, they suffer from inefficiencies in computational efficiency, high operational costs, and limitations in capturing temporal dependencies. To address these challenges, we propose the CMDMamba model, which is based on the Mamba architecture of state-space models (SSMs) and achieves near-linear time complexity. This significantly enhances the real -time data processing capability and reduces the deployment costs for risk management systems. The CMDMamba model employs a dual-layer Mamba structure that effectively captures price fluctuations at both the micro-and macrolevels in financial markets and integrates an innovative Dual Convolutional Feedforward Network (DconvFFN) module. This module is able to effectively capture the correlations between multiple variables in financial markets. By doing so, it provides more accurate time series modeling, optimizes algorithmic trading strategies, and facilitates investment portfolio risk warnings. Experiments conducted on four real-world financial datasets demonstrate that CMDMamba achieves a 10.4% improvement in prediction accuracy for multivariate forecasting tasks compared to state-of-the-art models. Moreover, it excels in both predictive accuracy and computational efficiency, setting a new benchmark in the field of financial time series forecasting.
Keywords: Mamba, Financial time series forecasting, deep learning, State space models, Computational efficiency
Received: 25 Mar 2025; Accepted: 20 Jun 2025.
Copyright: © 2025 Qin, Wei, Zhai, Lin, Yu and Jiang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Jingxuan Jiang, School of Business Administration, Guangxi Vocational \& Technical Institute of Industry, Nanning, China
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