让时间序列预测能模拟‘如果明天是世界杯决赛’这类假设场景。
What if Tomorrow is the World Cup Final? Counterfactual Time Series Forecasting with Textual Conditions
- 通过文本条件建模，实现对假设性未来事件的动态预测。
- 提出新评估框架，在无真实数据时仍可评测预测效果。
- 区分可变与不可变因素，提升复杂文本条件下的预测精度。
时间序列预测在现实场景中愈发关键，未来走势不仅受历史模式影响，还受即将发生的事件驱动。传统方法多依赖历史或确定性未来信息，忽略反事实情景，且常受限于简单结构化条件，难以应对真实世界的复杂性。为此，我们提出“带文本条件的反事实时间序列预测”任务，支持灵活、条件感知的预测。构建了涵盖事实与反事实设置的综合评估框架，即使缺乏真实时间序列也能进行有效评估。同时提出一种新颖的文本归因机制，区分可变与不可变因素，在复杂随机文本条件下显著提升预测准确性。项目页面见 https://seqml.github.io/TADiff/
原文摘要 · Abstract (English)
Time series forecasting has become increasingly critical in real-world scenarios, where future sequences are influenced not only by historical patterns but also by forthcoming events. In this context, forecasting must dynamically adapt to complex and stochastic future conditions, which introduces fundamental challenges in both forecasting and evaluation. Traditional methods typically rely on historical data or factual future conditions, while overlooking counterfactual scenarios. Furthermore, many existing approaches are restricted to simple structured conditions, limiting their ability to generalize to the real-world complexities. To address these gaps, we introduce the task of counterfactual time series forecasting with textual conditions, enabling more flexible and condition-aware forecasting. We propose a comprehensive evaluation framework that encompasses both factual and counterfactual settings, even in the absence of ground truth time series. Additionally, we present a novel text-attribution mechanism that distinguishes mutable from immutable factors, thereby improving forecast accuracy under sophisticated and stochastic textual conditions. The project page is at https://seqml.github.io/TADiff/
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