Intervention-Aware Forecasting: Breaking Historical Limits from a System Perspective

2024-05-22

AI Summary

Traditional time-series forecasting methods neglect the causal impact of external interventions—such as news articles or policy announcements—on dynamic system evolution, leading to suboptimal accuracy. This paper proposes the Intervention-Aware Time-Series Forecasting (IATSF) framework, the first to systematically model the causal effects of textual interventions. Our contributions are threefold: (1) we construct an information-leakage-free textual intervention benchmark; (2) we introduce Channel-Adaptive Sensitivity Modeling (CASM) and Channel-Adaptive Parameter Sharing (CAPS) to capture heterogeneous intervention effects across time-series channels; and (3) we design FIATS, a lightweight model integrating control-theoretic analysis with efficient textual encoding. Evaluated on both synthetic and real-world datasets, IATSF consistently outperforms state-of-the-art methods. Crucially, ablation studies confirm that explicit intervention modeling—not increased model complexity—is the primary driver of performance gains.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Planning, Routing, and Scheduling: Model-Based Reasoning

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Traditional time series forecasting methods predominantly rely on historical data patterns, neglecting external interventions that significantly shape future dynamics. Through control-theoretic analysis, we show that the implicit"self-stimulation"assumption limits the accuracy of these forecasts. To overcome this limitation, we propose an Intervention-Aware Time Series Forecasting (IATSF) framework explicitly designed to incorporate external interventions. We particularly emphasize textual interventions due to their unique capability to represent qualitative or uncertain influences inadequately captured by conventional exogenous variables. We propose a leak-free benchmark composed of temporally synchronized textual intervention data across synthetic and real-world scenarios. To rigorously evaluate IATSF, we develop FIATS, a lightweight forecasting model that integrates textual interventions through Channel-Aware Adaptive Sensitivity Modeling (CASM) and Channel-Aware Parameter Sharing (CAPS) mechanisms, enabling the model to adjust its sensitivity to interventions and historical data in a channel-specific manner. Extensive empirical evaluations confirm that FIATS surpasses state-of-the-art methods, highlighting that forecasting improvements stem explicitly from modeling external interventions rather than increased model complexity alone.

Problem

• Overcoming reliance on historical data by modeling external interventions
• Incorporating textual interventions for qualitative or uncertain influences
• Developing a lightweight model to adjust sensitivity to interventions

Innovation

• Intervention-Aware Time Series Forecasting framework
• Channel-Aware Adaptive Sensitivity Modeling mechanism
• Channel-Aware Parameter Sharing mechanism