CAE-FCM: Context-Aware Enhanced Fuzzy Cognitive Maps for Interpretable Multivariate Time Series Forecasting

Rui Hou, Yao Liu, Jingyu Cao, Xuanting Xie, Jingbo Wang, Qiao Liu

Abstract: Multivariate time series forecasting (MTSF) aims to predict future values based on historical observations. Recently, Fuzzy Cognitive Maps (FCMs) have emerged as a promising and interpretable approach for MTSF. However, existing FCM-based models suffer from two main limitations. First, their feature extraction mechanisms fail to effectively represent raw time series data, limiting the ability to capture complex spatiotemporal dependencies. Second, the single-variable composite modeling strategy adopted by high-order FCMs neglects holistic inter-variable relationships across time, leading to inefficiencies and a linear increase in model parameters. To overcome these challenges, we propose a novel framework–Context-Aware Enhanced FCM (CAE-FCM)–for interpretable MTSF. To address the first limitation, CAE-FCM introduces two complementary feature extraction modules: The Adaptive Graph Convolution module, which captures spatial dependencies through neighborhood-aware information aggregation, and the Global-Local Context-Aware Mamba module, which models temporal dependencies via a state space model that learns global and local temporal dynamics. For second limitation, CAE-FCM integrates the extracted spatial and temporal features into unified high-dimensional representations for FCM nodes, enabling efficient and expressive modeling of complex spatiotemporal interactions. Extensive experiments on five benchmark datasets demonstrate that CAE-FCM achieves state-of-the-art performance, significantly outperforming HFCM-based baselines in both forecasting accuracy and computational efficiency.

External IDs:dblp:journals/tfs/HouLCXWL26