TriAlignNet: A triple-path cross-modality alignment framework for multimodal time series forecasting

Area of Science:

• Artificial Intelligence
• Machine Learning
• Data Science

Background:

• Time series forecasting (TSF) is crucial in various domains but often limited by single-modality approaches.
• Existing multimodal TSF methods struggle with deep distribution heterogeneity and semantic inconsistency.
• Integrating auxiliary modalities like text is challenging due to complex cross-modal interactions.

Purpose of the Study:

• To propose a novel framework for aligning multimodal information in time series forecasting.
• To address the limitations of traditional single-stage fusion methods in handling multimodal time-series data.
• To enhance TSF performance by effectively integrating textual information with numerical time series.

Main Methods:

• Developed the Triple-Path Cross-Modality Alignment Framework (TriAlignNet) for multimodal TSF.
• Employed distribution-level alignment using Maximum Mean Discrepancy (MMD) to bridge statistical gaps.
• Utilized semantic-level alignment with a shared anchor matrix and kernel similarity mapping.
• Implemented interaction-level alignment via a multimodal Transformer for dynamic dependency modeling.

Main Results:

• TriAlignNet effectively aligns multimodal information, enhancing time series forecasting accuracy.
• The framework surpasses existing baseline methods in multimodal TSF tasks.
• Demonstrated robust performance in integrating textual information for improved temporal modeling.

Conclusions:

• TriAlignNet offers a robust and effective solution for multimodal time series forecasting.
• The proposed cross-modal progressive alignment framework advances the field of temporal modeling.
• The study highlights the potential of integrating diverse data modalities for superior forecasting outcomes.