Time-series forecasting is a critical challenge in various domains and has
witnessed substantial progress in recent years. Many real-life scenarios, such
as public health, economics, and social applications, involve feedback loops
where predictions can influence the predicted outcome, subsequently altering
the target variable's distribution. This phenomenon, known as performativity,
introduces the potential for 'self-negating' or 'self-fulfilling' predictions.
Despite extensive studies in classification problems across domains,
performativity remains largely unexplored in the context of time-series
forecasting from a machine-learning perspective.
In this paper, we formalize performative time-series forecasting (PeTS),
addressing the challenge of accurate predictions when performativity-induced
distribution shifts are possible. We propose a novel approach, Feature
Performative-Shifting (FPS), which leverages the concept of delayed response to
anticipate distribution shifts and subsequently predicts targets accordingly.
We provide theoretical insights suggesting that FPS can potentially lead to
reduced generalization error. We conduct comprehensive experiments using
multiple time-series models on COVID-19 and traffic forecasting tasks. The
results demonstrate that FPS consistently outperforms conventional time-series
forecasting methods, highlighting its efficacy in handling
performativity-induced challenges.

本文研究了时间序列预测中的表演性问题，在机器学习的视角下，提出了一种称为特征表演位移（FPS）的新方法，该方法利用延迟响应的概念来预测可能的分布变化，并相应地预测目标变量。通过在 COVID-19 和交通预测任务上进行综合实验，结果表明 FPS 始终优于传统时间序列预测方法，突出了其在应对表演性问题方面的效果。