← Back to all news
arXiv Machine Learning By Guillaume Coulaud (UM, IROKO), Reza Akbarinia (IROKO), Florent Masseglia (IROKO)

Disjoint or Overlapping? Inference Windowing for Reconstruction-Based Time Series Anomaly Detection

Read the original on arXiv Machine Learning →

arXiv:2606. 09874v1 Announce Type: new Abstract: Reconstruction-based methods are widely used for time series anomaly detection, where models are trained to reconstruct subsequences, and anomalies are identified through reconstruction errors.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

Related stories

arXiv AI
Jul 2

PaAno: Patch-Based Representation Learning for Time-Series Anomaly Detection

arXiv:2602. 01359v3 Announce Type: replace-cross Abstract: Although recent studies on time-series anomaly detection have increasingly adopted ever-larger neural network architectures such as transformers and foundation models, they incur high computational costs and memory usage, making them impractical for real-time and resource-constrained scenarios.

By Jinju Park, Seokho Kang
llmsragbenchmarks
More like this →
arXiv AI
Jun 12

ASTER: Latent Pseudo-Anomaly Generation for Unsupervised Time-Series Anomaly Detection

arXiv:2604. 13924v3 Announce Type: replace-cross Abstract: Time-series anomaly detection (TSAD) is critical in domains such as industrial monitoring, healthcare, and cybersecurity, but it remains challenging due to rare and heterogeneous anomalies and the scarcity of labelled data.

By Romain Hermary, Samet Hicsonmez, Dan Pineau, Abd El Rahman Shabayek, Djamila Aouada
llmsragdiffusionbenchmarks
More like this →
arXiv Machine Learning
Sep 24

CAST: Context- and Anomaly Structure-Conditioned Time Series Anomaly Generation

CAST is a framework for generating anomalous time series that addresses the scarcity and heterogeneity of anomaly data. It uses a two‑stage approach: pretraining on abundant normal data to learn system dynamics, then finetuning with anomaly structure representations to capture diverse anomaly morphologies. Experiments on real‑world datasets show that CAST outperforms existing methods in both generation quality and downstream task performance.

By Haochen Zhang, Jie Peng, Songyuan Sui, Yu-Chao Huang, Xiangqi Zhu, Tianlong Chen
fine-tuningbenchmarkssafety
More like this →