Abstract
INTRODUCTION: 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. METHODS: 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. RESULTS: 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. DISCUSSION: Moreover, CMDMamba excels in both predictive accuracy and computational efficiency, setting a new benchmark in the field of financial time series forecasting.