  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.
