INTARG: Informed Real-Time Adversarial Attack Generation for Time-Series Regression

TL;DR

This paper introduces INTARG, a targeted real-time adversarial attack method for time-series forecasting that efficiently increases prediction errors by focusing on high-confidence, high-error points.

Contribution

It presents a novel online, selective attack framework that significantly improves attack efficiency and effectiveness in time-series models.

Findings

Increases prediction error up to 2.42x

Performs attacks in less than 10% of time steps

Outperforms existing attack strategies in efficiency and impact

Abstract

Time-series forecasting aims to predict future values by modeling temporal dependencies in historical observations. It is a critical component of many real-world systems, where accurate forecasts improve operational efficiency and help mitigate uncertainty and risk. More recently, machine learning (ML), and especially deep learning (DL)-based models, have gained widespread adoption for time-series forecasting, but they remain vulnerable to adversarial attacks. However, many state-of-the-art attack methods are not directly applicable in time-series settings, where storing complete historical data or performing attacks at every time step is often impractical. This paper proposes an adversarial attack framework for time-series forecasting under an online bounded-buffer setting, leveraging an informed and selective attack strategy. By selectively targeting time steps where the model exhibits high confidence and the expected prediction error is maximal, our framework produces fewer but substantially more effective attacks. Experiments show that our framework can increase the prediction error up to 2.42x, while performing attacks in fewer than 10% of time steps.