Performative Time-Series Forecasting

Time-series forecasting is a critical challenge in various domains and has witnessed substantial progress in recent years. Many reallife scenarios, such as public health, economics, and social applications, involve feedback loops where predictive models can trigger actions that influence the outcome they aim to predict, subsequently altering the target variable's distribution. This phenomenon, known as performativity, introduces the potential for 'self-negating' or 'selffulfilling' predictions. Despite extensive studies on performativity in classification problems across domains, this phenomenon 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 for predictions when performativityinduced distribution shifts are possible. We propose a novel solution to PeTS, Feature Performative-Shifting (FPS), which leverages the concept of delayed response to anticipate distribution shifts and subsequently predicts targets. We provide theoretical insights suggesting that FPS potentially leads to reduced generalization error. Extensive experiment results demonstrate that FPS consistently outperforms conventional time-series forecasting and concept drift methods, highlighting its efficacy in handling performativity-induced challenges.