Signed Dual Attention: Capturing Signed Dependencies in Time Series Forecasting

Researchers introduce Signed Dual Attention, a novel transformer attention mechanism that captures both positive and negative dependencies in time series data without requiring additional parameters. By using a dual message-passing approach inspired by correlation structures, this technique achieves greater expressiveness while maintaining parameter efficiency, potentially improving forecasting accuracy in applications requiring signed relational modeling.

The development of Signed Dual Attention represents a meaningful refinement in transformer architecture design, addressing a fundamental limitation in how attention mechanisms process relational data. Traditional attention mechanisms assume homophilic interactions—where similar entities relate positively—but many real-world systems, particularly time series data, contain both supportive and contrastive relationships that standard architectures struggle to model effectively. This research tackles that gap by introducing a dual message-passing scheme that propagates both types of dependencies simultaneously within a single block, effectively doubling expressiveness without inflating the model's parameter count.

The innovation emerges from a broader recognition within the deep learning community that transformer architectures, while powerful for NLP tasks, require domain-specific adaptations for time series applications. Time series often exhibit complex patterns where certain variables move together while others move inversely—dynamics that standard attention fails to capture naturally. Signed Dual Attention bridges this gap by leveraging correlation structures to intelligently route positive and negative information flows.

For practitioners building time series forecasting systems, this work offers practical value by enabling parameter-efficient model improvements without architectural overhauls. Developers can integrate Signed Dual Attention into existing transformer-based forecasting systems, yielding performance gains particularly in scenarios with pronounced signed dependencies. The parameter efficiency aspect matters significantly for resource-constrained environments and large-scale deployments where model size directly impacts computational costs and inference latency.

• →Signed Dual Attention captures both positive and negative relational patterns in time series without additional parameters.
• →The mechanism uses dual message-passing inspired by correlation structures to propagate supportive and contrastive information simultaneously.
• →The approach achieves expressiveness equivalent to two-head attention while maintaining parameter efficiency.
• →The module integrates seamlessly into existing transformer architectures for improved forecasting performance.
• →The innovation addresses a fundamental limitation where standard transformers assume only homophilic interactions.