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

• URL: http://arxiv.org/abs/2604.11928v1
• Date: Mon, 13 Apr 2026 18:16:39 GMT
• Title: INTARG: Informed Real-Time Adversarial Attack Generation for Time-Series Regression
• Abstract summary: This paper proposes an adversarial attack framework for time-series forecasting under an online bounded-buffer setting.<n>We show that our framework can increase the prediction error up to 2.42x, while performing attacks in fewer than 10% of time steps.
• Score: 9.085996862368576
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Forecasting Fails: Unveiling Evasion Attacks in Weather Prediction Models [60.728124907335]
  This work introduces Weather Adaptive Adversarial Perturbation Optimization (WAAPO), a novel framework for generating targeted adversarial perturbations.<n>WAAPO achieves this by incorporating constraints for channel sparsity, spatial localization, and smoothness, ensuring that perturbations remain physically realistic and imperceptible.<n>Our experiments highlight critical vulnerabilities in AI-driven forecasting models, where small perturbations to initial conditions can result in significant deviations.
  arXiv  Detail & Related papers  (2025-12-09T17:20:56Z)
• SynCast: Synergizing Contradictions in Precipitation Nowcasting via Diffusion Sequential Preference Optimization [62.958457694151384]
  We introduce preference optimization into precipitation nowcasting for the first time, motivated by the success of reinforcement learning from human feedback in large language models.<n>In the first stage, the framework focuses on reducing FAR, training the model to effectively suppress false alarms.
  arXiv  Detail & Related papers  (2025-10-22T16:11:22Z)
• Accuracy Law for the Future of Deep Time Series Forecasting [65.46625911002202]
  Time series forecasting inherently faces a non-zero error lower bound due to its partially observable and uncertain nature.<n>This paper focuses on a fundamental question: how to estimate the performance upper bound of deep time series forecasting.<n>Based on rigorous statistical tests of over 2,800 newly trained deep forecasters, we discover a significant exponential relationship between the minimum forecasting error of deep models and the complexity of window-wise series patterns.
  arXiv  Detail & Related papers  (2025-10-03T05:18:47Z)
• KAIROS: Unified Training for Universal Non-Autoregressive Time Series Forecasting [6.312575071507716]
  We present KAIROS, a non-autoregressive time series forecasting framework.<n>Unlike autoregressive approaches, KAIROS avoids error accumulation and achieves just-in-time inference.
  arXiv  Detail & Related papers  (2025-10-02T14:50:50Z)
• Privacy Risks in Time Series Forecasting: User- and Record-Level Membership Inference [2.5510639595356595]
  We introduce two new membership inference attacks for time series forecasting.<n>We benchmark these methods against adapted versions of other leading attacks from the classification setting.<n>Our results show that forecasting models are vulnerable, with user-level attacks often achieving perfect detection.
  arXiv  Detail & Related papers  (2025-09-04T12:43:45Z)
• Fre-CW: Targeted Attack on Time Series Forecasting using Frequency Domain Loss [12.054571474020804]
  This paper proposes a time series prediction attack algorithm based on frequency domain loss.<n>To the best of our knowledge, there is no relevant research on using frequency information for time-series adversarial attacks.
  arXiv  Detail & Related papers  (2025-08-12T14:12:58Z)
• Loss Shaping Constraints for Long-Term Time Series Forecasting [79.3533114027664]
  We present a Constrained Learning approach for long-term time series forecasting that respects a user-defined upper bound on the loss at each time-step. We propose a practical Primal-Dual algorithm to tackle it, and aims to demonstrate that it exhibits competitive average performance in time series benchmarks, while shaping the errors across the predicted window.
  arXiv  Detail & Related papers  (2024-02-14T18:20:44Z)
• Performative Time-Series Forecasting [64.03865043422597]
  We formalize performative time-series forecasting (PeTS) from a machine-learning perspective.<n>We propose a novel approach, Feature Performative-Shifting (FPS), which leverages the concept of delayed response to anticipate distribution shifts.<n>We conduct comprehensive experiments using multiple time-series models on COVID-19 and traffic forecasting tasks.
  arXiv  Detail & Related papers  (2023-10-09T18:34:29Z)
• Targeted Attacks on Timeseries Forecasting [0.6719751155411076]
  We propose a novel formulation of Directional, Amplitudinal, and Temporal targeted adversarial attacks on time series forecasting models. These targeted attacks create a specific impact on the amplitude and direction of the output prediction. Our experimental results show how targeted attacks on time series models are viable and are more powerful in terms of statistical similarity.
  arXiv  Detail & Related papers  (2023-01-27T06:09:42Z)
• Robust Multivariate Time-Series Forecasting: Adversarial Attacks and Defense Mechanisms [17.75675910162935]
  A new attack pattern negatively impacts the forecasting of a target time series. We develop two defense strategies to mitigate the impact of such attack. Experiments on real-world datasets confirm that our attack schemes are powerful.
  arXiv  Detail & Related papers  (2022-07-19T22:00:41Z)
• Learning Interpretable Deep State Space Model for Probabilistic Time Series Forecasting [98.57851612518758]
  Probabilistic time series forecasting involves estimating the distribution of future based on its history. We propose a deep state space model for probabilistic time series forecasting whereby the non-linear emission model and transition model are parameterized by networks. We show in experiments that our model produces accurate and sharp probabilistic forecasts.
  arXiv  Detail & Related papers  (2021-01-31T06:49:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.