Copertina

Training-Free Time-Series Anomaly Detection: Leveraging Image Foundation Models

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Namura, Nobuo, Ichikawa, Yuma
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Regional Expected Improvement for Efficient Trust Region Selection in High-Dimensional Bayesian Optimization
di: Namura, Nobuo, et al.
Pubblicazione: (2024)

Best-Arm Identification-Based Trust Region Selection for Bayesian Optimization on Multimodal Functions
di: Namura, Nobuo, et al.
Pubblicazione: (2026)

Leveraging Intermediate Representations of Time Series Foundation Models for Anomaly Detection
di: Han, Chan Sik, et al.
Pubblicazione: (2025)

ChronosAD: Leveraging Time Series Foundation Models for Accurate Anomaly Detection
di: Khan, Uzair, et al.
Pubblicazione: (2026)

Quantization Error Propagation: Revisiting Layer-Wise Post-Training Quantization
di: Arai, Yamato, et al.
Pubblicazione: (2025)

Controlling Continuous Relaxation for Combinatorial Optimization
di: Ichikawa, Yuma
Pubblicazione: (2023)

Leveraging Time-Series Foundation Model for Subsurface Well Logs Prediction and Anomaly Detection
di: Koeshidayatullah, Ardiansyah, et al.
Pubblicazione: (2024)

Time Series Foundational Models: Their Role in Anomaly Detection and Prediction
di: Shyalika, Chathurangi, et al.
Pubblicazione: (2024)

TimeRadar: A Domain-Rotatable Foundation Model for Time Series Anomaly Detection
di: He, Hui, et al.
Pubblicazione: (2026)

Label-Free Multivariate Time Series Anomaly Detection
di: Zhou, Qihang, et al.
Pubblicazione: (2023)

Towards Foundation Models for Zero-Shot Time Series Anomaly Detection: Leveraging Synthetic Data and Relative Context Discrepancy
di: Lan, Tian, et al.
Pubblicazione: (2025)

RATFM: Retrieval-augmented Time Series Foundation Model for Anomaly Detection
di: Maru, Chihiro, et al.
Pubblicazione: (2025)

High-dimensional Asymptotics of VAEs: Threshold of Posterior Collapse and Dataset-Size Dependence of Rate-Distortion Curve
di: Ichikawa, Yuma, et al.
Pubblicazione: (2023)

THEMIS: Unlocking Pretrained Knowledge with Foundation Model Embeddings for Anomaly Detection in Time Series
di: Lorik, Yadav Mahesh, et al.
Pubblicazione: (2025)

A Comparative Study of Adaptation Strategies for Time Series Foundation Models in Anomaly Detection
di: Park, Miseon, et al.
Pubblicazione: (2026)

Towards Foundation Auto-Encoders for Time-Series Anomaly Detection
di: González, Gastón García, et al.
Pubblicazione: (2025)

Adaptive Conformal Anomaly Detection with Time Series Foundation Models for Signal Monitoring
di: Gil, Natalia Martinez, et al.
Pubblicazione: (2026)

STAR: Boosting Time Series Foundation Models for Anomaly Detection through State-aware Adapter
di: Cheng, Hanyin, et al.
Pubblicazione: (2025)

Optimization by Parallel Quasi-Quantum Annealing with Gradient-Based Sampling
di: Ichikawa, Yuma, et al.
Pubblicazione: (2024)

Continuous Parallel Relaxation for Finding Diverse Solutions in Combinatorial Optimization Problems
di: Ichikawa, Yuma, et al.
Pubblicazione: (2024)

TimeSeriesBench: An Industrial-Grade Benchmark for Time Series Anomaly Detection Models
di: Si, Haotian, et al.
Pubblicazione: (2024)

Leveraging Generic Time Series Foundation Models for EEG Classification
di: Gnassounou, Théo, et al.
Pubblicazione: (2025)

Sign Lock-In: Randomly Initialized Weight Signs Persist and Bottleneck Sub-Bit Model Compression
di: Sakai, Akira, et al.
Pubblicazione: (2026)

Selective Denoising Diffusion Model for Time Series Anomaly Detection
di: Obata, Kohei, et al.
Pubblicazione: (2026)

Single and Multi-Objective Optimization Benchmark Problems Focusing on Human-Powered Aircraft Design
di: Namura, Nobuo
Pubblicazione: (2023)

Statistical Mechanics of Min-Max Problems
di: Ichikawa, Yuma, et al.
Pubblicazione: (2024)

AXIS: Explainable Time Series Anomaly Detection with Large Language Models
di: Lan, Tian, et al.
Pubblicazione: (2025)

Contextual and Seasonal LSTMs for Time Series Anomaly Detection
di: Zhang, Lingpei, et al.
Pubblicazione: (2026)

Graph Anomaly Detection in Time Series: A Survey
di: Ho, Thi Kieu Khanh, et al.
Pubblicazione: (2023)

Open-Set Multivariate Time-Series Anomaly Detection
di: Lai, Thomas, et al.
Pubblicazione: (2023)

Forecast2Anomaly (F2A): Adapting Multivariate Time Series Foundation Models for Anomaly Prediction
di: Hassan, Atif, et al.
Pubblicazione: (2025)

Explainable Time Series Anomaly Detection using Masked Latent Generative Modeling
di: Lee, Daesoo, et al.
Pubblicazione: (2023)

Physics-Informed Diffusion Models for Unsupervised Anomaly Detection in Multivariate Time Series
di: Soni, Juhi, et al.
Pubblicazione: (2025)

Training-Free Zero-Shot Anomaly Detection in 3D Brain MRI with 2D Foundation Models
di: Le-Gia, Tai, et al.
Pubblicazione: (2026)

How Foundational are Foundation Models for Time Series Forecasting?
di: Karaouli, Nouha, et al.
Pubblicazione: (2025)

MIXAD: Memory-Induced Explainable Time Series Anomaly Detection
di: Kim, Minha, et al.
Pubblicazione: (2024)

Weakly Augmented Variational Autoencoder in Time Series Anomaly Detection
di: Wu, Zhangkai, et al.
Pubblicazione: (2024)

Multivariate Time Series Anomaly Detection in Industry 5.0
di: Colombi, Lorenzo, et al.
Pubblicazione: (2025)

Formally Exploring Time-Series Anomaly Detection Evaluation Metrics
di: Wagner, Dennis, et al.
Pubblicazione: (2025)

Open Challenges in Time Series Anomaly Detection: An Industry Perspective
di: Mueller, Andreas
Pubblicazione: (2025)