# Adversarial Examples in Deep Learning for Multivariate Time Series Regression

arXiv: https://arxiv.org/abs/2009.11911 · HTML: https://mlsanity.com/paper/2009.11911/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Sep 2020.

## Built on by (most cited first, 26 of 26)

- [BACKTIME: Backdoor Attacks on Multivariate Time Series Forecasting](https://mlsanity.com/paper/2410.02195/) (2024)
- [When and How to Fool Explainable Models (and Humans) with Adversarial Examples](https://mlsanity.com/paper/2107.01943/) (2021)
- [Robust Multivariate Time-Series Forecasting: Adversarial Attacks and Defense Mechanisms](https://mlsanity.com/paper/2207.09572/) (2022)
- [Targeted Attacks on Timeseries Forecasting](https://mlsanity.com/paper/2301.11544/) (2023)
- [Adversarial Framework with Certified Robustness for Time-Series Domain via Statistical Features](https://mlsanity.com/paper/2207.04307/) (2022)
- [Physical Passive Patch Adversarial Attacks on Visual Odometry Systems](https://mlsanity.com/paper/2207.05729/) (2022)
- [Robustness Testing of Data and Knowledge Driven Anomaly Detection in Cyber-Physical Systems](https://mlsanity.com/paper/2204.09183/) (2022)
- [Artificial Intelligence Based Prognostic Maintenance of Renewable Energy Systems: A Review of Techniques, Challenges, and Future Research Directions](https://mlsanity.com/paper/2104.12561/) (2021)
- [Adversarial News and Lost Profits: Manipulating Headlines in LLM-Driven Algorithmic Trading](https://mlsanity.com/paper/2601.13082/) (2026)
- [On the Susceptibility and Robustness of Time Series Models through Adversarial Attack and Defense](https://mlsanity.com/paper/2301.03703/) (2023)
- [Adversarial Attacks and Defenses in Multivariate Time-Series Forecasting for Smart and Connected Infrastructures](https://mlsanity.com/paper/2408.14875/) (2024)
- [Securing Virtual Reality Experiences: Unveiling and Tackling Cybersickness Attacks with Explainable AI](https://mlsanity.com/paper/2503.13419/) (2025)
- [Empirical Analysis of Adversarial Robustness and Explainability Drift in Cybersecurity Classifiers](https://mlsanity.com/paper/2602.06395/) (2026)
- [Temporally Unified Adversarial Perturbations for Time Series Forecasting](https://mlsanity.com/paper/2602.11940/) (2026)
- [Evaluating Deep Learning Models and Adversarial Attacks on Accelerometer-Based Gesture Authentication](https://mlsanity.com/paper/2110.14597/) (2021)
- [RobustPdM: Designing Robust Predictive Maintenance against Adversarial Attacks](https://mlsanity.com/paper/2301.10822/) (2023)
- [Wasserstein Adversarial Examples on Univariant Time Series Data](https://mlsanity.com/paper/2303.12357/) (2023)
- [Using ARIMA to Predict the Expansion of Subscriber Data Consumption](https://mlsanity.com/paper/2404.15095/) (2024)
- [Federated Learning for Anomaly Detection in Energy Consumption Data: Assessing the Vulnerability to Adversarial Attacks](https://mlsanity.com/paper/2502.05041/) (2025)
- [Analysis of the vulnerability of machine learning regression models to adversarial attacks using data from 5G wireless networks](https://mlsanity.com/paper/2505.00487/) (2025)
- [Are Inherently Interpretable Models More Robust? A Study In Music Emotion Recognition](https://mlsanity.com/paper/2508.03780/) (2025)
- [Fre-CW: Targeted Attack on Time Series Forecasting using Frequency Domain Loss](https://mlsanity.com/paper/2508.08955/) (2025)
- [Adversarial VR: An Open-Source Testbed for Evaluating Adversarial Robustness of VR Cybersickness Detection and Mitigation](https://mlsanity.com/paper/2512.17029/) (2025)
- [Exposing Vulnerabilities in Explanation for Time Series Classifiers via Dual-Target Attacks](https://mlsanity.com/paper/2602.02763/) (2026)
- [Adversarial Robustness of Time-Series Classification for Crystal Collimator Alignment](https://mlsanity.com/paper/2604.06289/) (2026)
- [Learning Defensive Policies against Diverse Inference Attacks for Smart Meter Privacy](https://mlsanity.com/paper/2609.25484/) (2026)

## Abstract (arXiv)

Multivariate time series (MTS) regression tasks are common in many real-world data mining applications including finance, cybersecurity, energy, healthcare, prognostics, and many others. Due to the tremendous success of deep learning (DL) algorithms in various domains including image recognition and computer vision, researchers started adopting these techniques for solving MTS data mining problems, many of which are targeted for safety-critical and cost-critical applications. Unfortunately, DL algorithms are known for their susceptibility to adversarial examples which also makes the DL regression models for MTS forecasting also vulnerable to those attacks. To the best of our knowledge, no previous work has explored the vulnerability of DL MTS regression models to adversarial time series examples, which is an important step, specifically when the forecasting from such models is used in safety-critical and cost-critical applications. In this work, we leverage existing adversarial attack generation techniques from the image classification domain and craft adversarial multivariate time series examples for three state-of-the-art deep learning regression models, specifically Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). We evaluate our study using Google stock and household power consumption dataset. The obtained results show that all the evaluated DL regression models are vulnerable to adversarial attacks, transferable, and thus can lead to catastrophic consequences in safety-critical and cost-critical domains, such as energy and finance.