Performative Time-Series Forecasting

TL;DR

This paper introduces a formal framework for performative time-series forecasting, proposing a novel method called FPS that anticipates distribution shifts caused by performativity, and demonstrates its effectiveness through experiments on COVID-19 and traffic data.

Contribution

It formalizes performative effects in time-series forecasting and proposes FPS, a new approach that anticipates distribution shifts to improve prediction accuracy.

Findings

FPS outperforms traditional methods in experiments

Performativity causes significant distribution shifts in time-series data

Theoretical analysis suggests FPS reduces generalization error

Abstract

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.