Are Time-Series Foundation Models Deployment-Ready? A Systematic Study of Adversarial Robustness Across Domains

• URL: http://arxiv.org/abs/2505.19397v1
• Date: Mon, 26 May 2025 01:24:11 GMT
• Title: Are Time-Series Foundation Models Deployment-Ready? A Systematic Study of Adversarial Robustness Across Domains
• Abstract summary: Time Series Foundation Models (TSFMs) are pretrained on large-scale, cross-domain data and capable of zero-shot forecasting in new scenarios without further training.<n>Are TSFMs robust to adversarial input perturbations?<n>These perturbations could be exploited in man-in-the-middle attacks or data poisoning.
• Score: 23.9530536685668
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Time Series Foundation Models (TSFMs), which are pretrained on large-scale, cross-domain data and capable of zero-shot forecasting in new scenarios without further training, are increasingly adopted in real-world applications. However, as the zero-shot forecasting paradigm gets popular, a critical yet overlooked question emerges: Are TSFMs robust to adversarial input perturbations? Such perturbations could be exploited in man-in-the-middle attacks or data poisoning. To address this gap, we conduct a systematic investigation into the adversarial robustness of TSFMs. Our results show that even minimal perturbations can induce significant and controllable changes in forecast behaviors, including trend reversal, temporal drift, and amplitude shift, posing serious risks to TSFM-based services. Through experiments on representative TSFMs and multiple datasets, we reveal their consistent vulnerabilities and identify potential architectural designs, such as structural sparsity and multi-task pretraining, that may improve robustness. Our findings offer actionable guidance for designing more resilient forecasting systems and provide a critical assessment of the adversarial robustness of TSFMs.

Related papers

• Exploring Zero-Shot Foundation Models for Multivariate Time Series Anomaly Detection [4.468174618435543]
  MTSAD is essential for reliability and safety in domains such as industrial process monitoring and financial risk management.<n>Foundation Models (FMs) are pre-trained on broad data with strong zero-shot generalization.<n>We investigate the zero-shot application of a FM to industrial MTSAD on the Secure Water Treatment (SWaT) benchmark.
  arXiv  Detail & Related papers  (2026-07-14T07:32:08Z)
• Factorize to Generalize: Retrieval-Guided Invariant-Dynamic Decomposition for Time Series Forecasting [64.2640656619583]
  Time series foundation models (TSFMs) have recently achieved strong zero-shot forecasting performance through large-scale pretraining and retrieval-augmented prediction.<n>We propose a Retrieval-guided Invariant-Dynamic DEcomposition framework for time series forecasting.
  arXiv  Detail & Related papers  (2026-05-24T07:31:55Z)
• Mitigating Error Amplification in Fast Adversarial Training [58.74042726356826]
  Fast Adversarial Training (FAT) has proven effective in enhancing model robustness by encouraging networks to learn perturbation-invariant representations.<n>FAT often suffers from catastrophic overfitting (CO), where the model overfits to the training attack and fails to generalize to unseen ones.<n>We propose a Distribution-aware Dynamic Guidance (DDG) strategy that dynamically adjusts both the perturbation budget and supervision signal.
  arXiv  Detail & Related papers  (2026-04-27T11:23:18Z)
• MEMTS: Internalizing Domain Knowledge via Parameterized Memory for Retrieval-Free Domain Adaptation of Time Series Foundation Models [51.506429027626005]
  Memory for Time Series (MEMTS) is a lightweight and plug-and-play method for retrieval-free domain adaptation in time series forecasting.<n>Key component of MEMTS is a Knowledge Persistence Module (KPM), which internalizes domain-specific temporal dynamics.<n>This paradigm shift enables MEMTS to achieve accurate domain adaptation with constant-time inference and near-zero latency.
  arXiv  Detail & Related papers  (2026-02-14T14:00:06Z)
• Real-Time Proactive Anomaly Detection via Forward and Backward Forecast Modeling [0.0]
  We introduce two proactive anomaly detection frameworks: the Forward Forecasting Model (FFM) and the Backward Reconstruction Model (BRM)<n>FFM forecasts future sequences to anticipate disruptions, while BRM reconstructs recent history from future context to uncover early precursors.<n>Our models support both continuous and discrete multivariate features, enabling robust performance in real-world settings.
  arXiv  Detail & Related papers  (2026-02-12T03:57:41Z)
• Synapse: Adaptive Arbitration of Complementary Expertise in Time Series Foundational Models [50.877082340479085]
  We study how different Time Series Foundational Models (TSFMs) exhibit specialized performance profiles across various forecasting settings.<n>We propose Synapse, a novel arbitration framework for TSFMs.<n>Results demonstrate that Synapse consistently outperforms other popular ensembling techniques as well as individual TSFMs.
  arXiv  Detail & Related papers  (2025-11-07T18:01:51Z)
• Time Series Foundation Models: Benchmarking Challenges and Requirements [0.0]
  Time Series Foundation Models (TSFMs) represent a new paradigm for time series forecasting.<n> evaluating TSFMs is tricky, as with ever more extensive training sets, it becomes more challenging to ensure integrity benchmarking data.
  arXiv  Detail & Related papers  (2025-10-15T15:15:45Z)
• A Unified Frequency Domain Decomposition Framework for Interpretable and Robust Time Series Forecasting [81.73338008264115]
  Current approaches for time series forecasting, whether in the time or frequency domain, predominantly use deep learning models based on linear layers or transformers.<n>We propose FIRE, a unified frequency domain decomposition framework that provides a mathematical abstraction for diverse types of time series.<n>Fire consistently outperforms state-of-the-art models on long-term forecasting benchmarks.
  arXiv  Detail & Related papers  (2025-10-11T09:59:25Z)
• Are Time Series Foundation Models Susceptible to Catastrophic Forgetting? [2.692427265051276]
  Time Series Foundation Models (TSFMs) have shown promising zero-shot generalization across diverse forecasting tasks.<n>We investigate the extent to which TSFMs suffer from catastrophic forgetting when fine-tuned sequentially on multiple datasets.
  arXiv  Detail & Related papers  (2025-10-01T12:14:34Z)
• Revisiting Multivariate Time Series Forecasting with Missing Values [65.30332997607141]
  Missing values are common in real-world time series.<n>Current approaches have developed an imputation-then-prediction framework that uses imputation modules to fill in missing values, followed by forecasting on the imputed data.<n>This framework overlooks a critical issue: there is no ground truth for the missing values, making the imputation process susceptible to errors that can degrade prediction accuracy.<n>We introduce Consistency-Regularized Information Bottleneck (CRIB), a novel framework built on the Information Bottleneck principle.
  arXiv  Detail & Related papers  (2025-09-27T20:57:48Z)
• Elucidated Rolling Diffusion Models for Probabilistic Weather Forecasting [52.6508222408558]
  We introduce Elucidated Rolling Diffusion Models (ERDM)<n>ERDM is the first framework to unify a rolling forecast structure with the principled, performant design of Elucidated Diffusion Models (EDM)<n>On 2D Navier-Stokes simulations and ERA5 global weather forecasting at 1.5circ resolution, ERDM consistently outperforms key diffusion-based baselines.
  arXiv  Detail & Related papers  (2025-06-24T21:44:31Z)
• Can Time-Series Foundation Models Perform Building Energy Management Tasks? [5.450531952940644]
  Building energy management tasks require processing and learning from a variety of time-series data.<n>Existing solutions rely on bespoke task- and data-specific models to perform these tasks.<n>Inspired by the transformative success of Large Language Models (LLMs), Time-Series Foundation Models (TSFMs) have the potential to change this.
  arXiv  Detail & Related papers  (2025-06-12T19:45:10Z)
• Probabilistic QoS Metric Forecasting in Delay-Tolerant Networks Using Conditional Diffusion Models on Latent Dynamics [5.038401442188212]
  Active metric prediction, commonly employed in the maintenance and operation of DTN, could enhance network performance regarding latency, throughput, energy consumption, and dependability.<n>Traditional mean regression methods for time series forecasting cannot capture the data complexity adequately, resulting in deteriorated performance in operational tasks in DTNs such as routing.<n>This paper formulates the prediction of metrics in DTN as a probabilistic forecasting problem on multivariate time series, where one could quantify the uncertainty of forecasts by characterizing the distribution of these samples.
  arXiv  Detail & Related papers  (2025-04-09T15:40:02Z)
• Time Series Foundational Models: Their Role in Anomaly Detection and Prediction [0.0]
  Time series foundational models (TSFM) have gained prominence in time series forecasting.<n>This paper critically evaluates the efficacy of TSFM in anomaly detection and prediction tasks.
  arXiv  Detail & Related papers  (2024-12-26T17:15:30Z)
• Future-Guided Learning: A Predictive Approach To Enhance Time-Series Forecasting [4.866362841501992]
  We introduce Future-Guided Learning, an approach that enhances time-series event forecasting through a dynamic feedback mechanism inspired by predictive coding.<n>Our method involves two models: a detection model that analyzes future data to identify critical events and a forecasting model that predicts these events based on current data.<n>We validate our approach on a variety of tasks, demonstrating a 44.8% increase in AUC-ROC for seizure prediction using EEG data, and a 48.7% reduction in MSE for forecasting in nonlinear dynamical systems.
  arXiv  Detail & Related papers  (2024-10-19T21:22:55Z)
• Multi-Knowledge Fusion Network for Time Series Representation Learning [2.368662284133926]
  We propose a hybrid architecture that combines prior knowledge with implicit knowledge of the relational structure within the MTS data. The proposed architecture has shown promising results on multiple benchmark datasets and outperforms state-of-the-art forecasting methods by a significant margin.
  arXiv  Detail & Related papers  (2024-08-22T14:18:16Z)
• Towards Evaluating the Robustness of Visual State Space Models [63.14954591606638]
  Vision State Space Models (VSSMs) have demonstrated remarkable performance in visual perception tasks. However, their robustness under natural and adversarial perturbations remains a critical concern. We present a comprehensive evaluation of VSSMs' robustness under various perturbation scenarios.
  arXiv  Detail & Related papers  (2024-06-13T17:59:44Z)
• Rating Multi-Modal Time-Series Forecasting Models (MM-TSFM) for Robustness Through a Causal Lens [10.103561529332184]
  We focus on multi-modal time-series forecasting, where imprecision due to noisy or incorrect data can lead to erroneous predictions. We introduce a rating methodology to assess the robustness of Multi-Modal Time-Series Forecasting Models.
  arXiv  Detail & Related papers  (2024-06-12T17:39:16Z)
• SFANet: Spatial-Frequency Attention Network for Weather Forecasting [54.470205739015434]
  Weather forecasting plays a critical role in various sectors, driving decision-making and risk management. Traditional methods often struggle to capture the complex dynamics of meteorological systems. We propose a novel framework designed to address these challenges and enhance the accuracy of weather prediction.
  arXiv  Detail & Related papers  (2024-05-29T08:00:15Z)
• Performative Time-Series Forecasting [71.18553214204978]
  We formalize performative time-series forecasting (PeTS) from a machine-learning perspective. We propose a novel approach, Feature Performative-Shifting (FPS), which leverages the concept of delayed response to anticipate distribution shifts. 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)
• DiffSTG: Probabilistic Spatio-Temporal Graph Forecasting with Denoising Diffusion Models [53.67562579184457]
  This paper focuses on probabilistic STG forecasting, which is challenging due to the difficulty in modeling uncertainties and complex dependencies. We present the first attempt to generalize the popular denoising diffusion models to STGs, leading to a novel non-autoregressive framework called DiffSTG. Our approach combines the intrinsic-temporal learning capabilities STNNs with the uncertainty measurements of diffusion models.
  arXiv  Detail & Related papers  (2023-01-31T13:42:36Z)
• 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.