Do Time-Series Foundation Models Pay Off for Industrial Monitoring? A Cost-Aware Empirical Study

• URL: http://arxiv.org/abs/2608.22968v1
• Date: Mon, 24 Aug 2026 08:30:06 GMT
• Title: Do Time-Series Foundation Models Pay Off for Industrial Monitoring? A Cost-Aware Empirical Study
• Abstract summary: Time-series foundation models (TSFMs) promise reusable representations and zero-shot forecasts.<n>This work presents a protocol-aware empirical assessment across three settings.
• Score: 1.8419890068150178
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Industrial monitoring models must detect operationally relevant deviations while satisfying target-specific data, calibration, and resource constraints. Time-series foundation models (TSFMs) promise reusable representations and zero-shot forecasts, yet evidence for their deployment value remains mixed when task definitions are heterogeneous and lightweight baselines are competitive. This work presents a protocol-aware empirical assessment across three settings: a C-MAPSS degradation-risk proxy, normal-only training for anomalous-sound detection on MIMII, and BDG2 forecasting-residual diagnostics with synthetic target perturbations. We assess classical one-class methods, compact neural autoencoders, residual forecasters, MOMENT-small, Chronos-T5, and TimesFM 2.5 in terms of anomaly-ranking performance, risk-horizon sensitivity, residual forecasting and perturbation sensitivity, and local implementation cost. Across 100 C-MAPSS engines evaluated out of fold, TCN-AE reaches fold-weighted AUROC/AUPRC 0.9570/0.8960, compared with 0.7310/0.3080 for MOMENT reconstruction; paired engine-cluster bootstrap confidence intervals exclude zero for both differences. Across five matched MIMII pump evaluations, OCSVM also exceeds MOMENT reconstruction in AUROC and AUPRC. On a fixed 12-meter BDG2 panel, TimesFM 2.5 has the lowest aligned forecast error and the highest synthetic AUROC point estimate, although synthetic AUPRC is similar across TSFM and fitted residual models. Same-device measurements show that MOMENT incurs higher latency, peak allocated VRAM, and serialized state-dictionary size than TCN-AE. Under the evaluated frozen and zero-shot settings, TSFMs are task-dependent deployment options rather than default replacements for fitted lightweight models.

Related papers

• IPM-FM: A Foundation Model with Consensus Feature Selection for Industrial Process Monitoring [59.59850511287819]
  We propose the industrial process monitoring foundation model (IPM-FM)<n>It first learns general-purpose representations from unlabeled industrial process data through self-supervised pretraining.<n>It adapts to specific monitoring tasks using a small amount of task-labeled data, and finally produces calibrated predictions.
  arXiv  Detail & Related papers  (2026-09-08T07:43:04Z)
• Large-Small Model Collaboration for Zero-Shot Surgical Phase Recognition [45.191945465591374]
  Task-specific lightweight models for surgical phase recognition excel at capturing temporal dynamics but generalize poorly under domain shift.<n>We propose textbfLarge-textbfSmall textbfTemporal adaptation (textbfLaST), a novel large-small collaborative framework that enables zero-shot adaptation to unseen clinical domains.
  arXiv  Detail & Related papers  (2026-08-24T07:06:52Z)
• Zero-Shot Heart Rate Variability Forecasting from Consumer Wearables Using Time Series Foundation Models [5.572607261284016]
  Short-term Heart Rate Variability (HRV) forecasting could provide clinicians with actionable lead time for detecting autonomic dysfunction and adverse cardiac events.<n> Consumer wearable devices generate fragmented, artifact-rich HRV signals that challenge conventional forecasting approaches.<n>In this study, we evaluated the forecasting ability of three Time Series Foundation Models (TSFMs), TimesFM, Chronos, and MOIRAI.
  arXiv  Detail & Related papers  (2026-07-22T11:12:07Z)
• Modeling Normal Is All You Need: Joint Latent Clustering for Anomaly Detection in Multimodal Cyber-Physical Systems [47.027290803102666]
  Normal behaviour is the union of many imbalanced, curved, thin-fringed operating regimes.<n>We model the normal law with a jointly learned latent representation plus explicit Gaussian-mixture mode clustering.<n>We evaluate under a deliberately fair protocol: raw point-wise metrics with no point adjustment, a trivial-detector difficulty split, prevalence-matched F1, and train-normal-only calibration.
  arXiv  Detail & Related papers  (2026-07-07T10:10:00Z)
• Anomaly Detection in IEC-61850 GOOSE Networks: Evaluating Unsupervised and Temporal Learning for Real-Time Intrusion Detection [0.0]
  The IEC-61850 GOOSE protocol underpins time-critical communication in modern digital substations.<n>Intrusion detection in this setting is challenging due to strict latency constraints (sub-4ms) and limited availability of labeled attack data.<n>This paper evaluates whether unsupervised temporal modeling can provide effective and deployable anomaly detection for GOOSE networks.
  arXiv  Detail & Related papers  (2026-04-14T20:13:55Z)
• 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)
• Echo State Networks for Time Series Forecasting: Hyperparameter Sweep and Benchmarking [51.56484100374058]
  We evaluate whether a fully automatic, purely feedback-driven ESN can serve as a competitive alternative to widely used statistical forecasting methods.<n>Forecast accuracy is measured using MASE and sMAPE and benchmarked against simple benchmarks like drift and seasonal naive and statistical models.
  arXiv  Detail & Related papers  (2026-02-03T16:01:22Z)
• EVEREST: An Evidential, Tail-Aware Transformer for Rare-Event Time-Series Forecasting [4.551615447454767]
  EVEREST is a transformer-based architecture for probabilistic rare-event forecasting.<n>It delivers calibrated predictions and tail-aware risk estimation.<n>It is applicable to high-stakes domains such as industrial monitoring, weather, and satellite diagnostics.
  arXiv  Detail & Related papers  (2026-01-26T23:15:20Z)
• Agentic World Modeling for 6G: Near-Real-Time Generative State-Space Reasoning [70.56067503630486]
  We argue that sixth-generation (6G) intelligence is not fluent token prediction but calibrated the capacity to imagine and choose.<n>We show that WM-MS3M cuts mean absolute error (MAE) by 1.69% versus MS3M with 32% fewer parameters and similar latency, and achieves 35-80% lower root mean squared error (RMSE) than attention/hybrid baselines with 2.3-4.1x faster inference.
  arXiv  Detail & Related papers  (2025-11-04T17:22:22Z)
• CoRA: Covariate-Aware Adaptation of Time Series Foundation Models [47.20786327020571]
  Time Series Foundation Models (TSFMs) have shown significant impact through their model capacity, scalability, and zero-shot generalizations.<n>We propose a general covariate-aware adaptation (CoRA) framework for TSFMs.
  arXiv  Detail & Related papers  (2025-10-14T16:20:00Z)
• Benchmarking Machine Learning Models for Fault Classification and Localization in Power System Protection [10.403249318465468]
  This work presents a comparative benchmarking study of classical machine learning models for fault classification (FC) and fault localization (FL) in power system protection based on EMT data.<n>The best-performing FC model achieved an F1 score of 0.992$pm$0.001, while the top FL model reached an R2 of 0.806$pm$0.008 with a mean processing time of 0.563 ms.
  arXiv  Detail & Related papers  (2025-10-01T12:44:14Z)
• CRTRE: Causal Rule Generation with Target Trial Emulation Framework [47.2836994469923]
  We introduce a novel method called causal rule generation with target trial emulation framework (CRTRE) CRTRE applies randomize trial design principles to estimate the causal effect of association rules. We then incorporate such association rules for the downstream applications such as prediction of disease onsets.
  arXiv  Detail & Related papers  (2024-11-10T02:40:06Z)

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.