MGTS-Net: Exploring Graph-Enhanced Multimodal Fusion for Augmented Time Series Forecasting

• URL: http://arxiv.org/abs/2510.16350v1
• Date: Sat, 18 Oct 2025 04:47:10 GMT
• Title: MGTS-Net: Exploring Graph-Enhanced Multimodal Fusion for Augmented Time Series Forecasting
• Authors: Shule Hao, Junpeng Bao, Wenli Li,
• Abstract summary: We propose MGTS-Net, a Multimodal Graph-enhanced Network for Time Series forecasting.<n>The model consists of three core components: (1) a Multimodal Feature Extraction layer (MFE), (2) a Multimodal Feature Fusion layer (MFF), and (3) a Multi-Scale Prediction layer (MSP)
• Score: 1.7077661158850292
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent research in time series forecasting has explored integrating multimodal features into models to improve accuracy. However, the accuracy of such methods is constrained by three key challenges: inadequate extraction of fine-grained temporal patterns, suboptimal integration of multimodal information, and limited adaptability to dynamic multi-scale features. To address these problems, we propose MGTS-Net, a Multimodal Graph-enhanced Network for Time Series forecasting. The model consists of three core components: (1) a Multimodal Feature Extraction layer (MFE), which optimizes feature encoders according to the characteristics of temporal, visual, and textual modalities to extract temporal features of fine-grained patterns; (2) a Multimodal Feature Fusion layer (MFF), which constructs a heterogeneous graph to model intra-modal temporal dependencies and cross-modal alignment relationships and dynamically aggregates multimodal knowledge; (3) a Multi-Scale Prediction layer (MSP), which adapts to multi-scale features by dynamically weighting and fusing the outputs of short-term, medium-term, and long-term predictors. Extensive experiments demonstrate that MGTS-Net exhibits excellent performance with light weight and high efficiency. Compared with other state-of-the-art baseline models, our method achieves superior performance, validating the superiority of the proposed methodology.

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