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

Source: [INTARG: Informed Real-Time Adversarial Attack Generation for Time-Series Regression](<https://www.sota2.com/research/paper/69df1b967d0172140ec7b905>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-04-16T05:00:04.684Z

Authors: Gamze Kirman Tokgoz, Onat Gungor, Tajana Rosing, Baris Aksanli

Published: 2026-04-13T18:16:39.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Adversarial Attack on Time-Series Forecasting on Household Electric Power Consumption (test)](<https://www.sota2.com/research/sota/adversarial-attack-on-time-series-forecasting-on-household-electric-power-consumption-test.md>): Adversarial Attack on Time-Series Forecasting; Household Electric Power Consumption (test); Attack RMSE: 0.253.

- [Adversarial Attack on Time-Series Forecasting on Pecan Street Database (test)](<https://www.sota2.com/research/sota/adversarial-attack-on-time-series-forecasting-on-pecan-street-database-test.md>): Adversarial Attack on Time-Series Forecasting; Pecan Street Database (test); Attack RMSE: 0.23.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2604.11928>)

[PDF](<https://arxiv.org/pdf/2604.11928.pdf>)