# Targeted Attacks on Timeseries Forecasting

arXiv: https://arxiv.org/abs/2301.11544 · HTML: https://mlsanity.com/paper/2301.11544/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 32 references that are in the map.
- First posted on arXiv 27 Jan 2023.

## Builds on (most cited first, 17 of 17)

- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Adversarial examples in the physical world](https://mlsanity.com/paper/1607.02533/) (2016)
- [Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks](https://mlsanity.com/paper/2003.01690/) (2020)
- [N-BEATS: Neural basis expansion analysis for interpretable time series forecasting](https://mlsanity.com/paper/1905.10437/) (2019)
- [A Multi-Horizon Quantile Recurrent Forecaster](https://mlsanity.com/paper/1711.11053/) (2017)
- [Multivariate Probabilistic Time Series Forecasting via Conditioned Normalizing Flows](https://mlsanity.com/paper/2002.06103/) (2020)
- [Conditional Time Series Forecasting with Convolutional Neural Networks](https://mlsanity.com/paper/1703.04691/) (2017)
- [Mitigating Evasion Attacks to Deep Neural Networks via Region-based Classification](https://mlsanity.com/paper/1709.05583/) (2017)
- [Adversarial Attacks on Deep Neural Networks for Time Series Classification](https://mlsanity.com/paper/1903.07054/) (2019)
- [Adversarial Attacks on Time Series](https://mlsanity.com/paper/1902.10755/) (2019)
- [Adversarial Deep Ensemble: Evasion Attacks and Defenses for Malware Detection](https://mlsanity.com/paper/2006.16545/) (2020)
- [Adversarial Examples in Deep Learning for Multivariate Time Series Regression](https://mlsanity.com/paper/2009.11911/) (2020)
- [Untargeted, Targeted and Universal Adversarial Attacks and Defenses on Time Series](https://mlsanity.com/paper/2101.05639/) (2021)
- [Adversarial Attacks on Probabilistic Autoregressive Forecasting Models](https://mlsanity.com/paper/2003.03778/) (2020)
- [CNNPred: CNN-based stock market prediction using several data sources](https://mlsanity.com/paper/1810.08923/) (2018)

## Built on by (most cited first, 8 of 8)

- [Adversarial Attacks and Defenses in Multivariate Time-Series Forecasting for Smart and Connected Infrastructures](https://mlsanity.com/paper/2408.14875/) (2024)
- [Enhancing TinyML Security: Study of Adversarial Attack Transferability](https://mlsanity.com/paper/2407.11599/) (2024)
- [INTARG: Informed Real-Time Adversarial Attack Generation for Time-Series Regression](https://mlsanity.com/paper/2604.11928/) (2026)
- [Creating a Causally Grounded Rating Method for Assessing the Robustness of AI Models for Time-Series Forecasting](https://mlsanity.com/paper/2502.12226/) (2025)
- [Fre-CW: Targeted Attack on Time Series Forecasting using Frequency Domain Loss](https://mlsanity.com/paper/2508.08955/) (2025)
- [Keep the Lights On, Keep the Lengths in Check: Plug-In Adversarial Detection for Time-Series LLMs in Energy Forecasting](https://mlsanity.com/paper/2512.12154/) (2025)
- [Exposing Vulnerabilities in Explanation for Time Series Classifiers via Dual-Target Attacks](https://mlsanity.com/paper/2602.02763/) (2026)
- [MorphStrata: Layer-Specific Perturbations for Generating Morphence Students in Time-Series Moving Target Defense](https://mlsanity.com/paper/2606.17435/) (2026)

## Abstract (arXiv)

Real-world deep learning models developed for Time Series Forecasting are used in several critical applications ranging from medical devices to the security domain. Many previous works have shown how deep learning models are prone to adversarial attacks and studied their vulnerabilities. However, the vulnerabilities of time series models for forecasting due to adversarial inputs are not extensively explored. While the attack on a forecasting model might aim to deteriorate the performance of the model, it is more effective, if the attack is focused on a specific impact on the model's output. In this paper, 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. We use the existing adversarial attack techniques from the computer vision domain and adapt them for time series. Additionally, we propose a modified version of the Auto Projected Gradient Descent attack for targeted attacks. We examine the impact of the proposed targeted attacks versus untargeted attacks. We use KS-Tests to statistically demonstrate the impact of the attack. Our experimental results show how targeted attacks on time series models are viable and are more powerful in terms of statistical similarity. It is, hence difficult to detect through statistical methods. We believe that this work opens a new paradigm in the time series forecasting domain and represents an important consideration for developing better defenses.