Marc Pinet

Research

Publications

2026
Anomalies in Multivariate Time Series Benchmarks Are Mostly Univariate
Marc Pinet, Julien Cumin, Samuel Berlemont, Dominique Vaufreydaz
KDD 2026, Mining and Learning from Time Series Workshop (MILETS), Jeju, South KoreaOralaccepted
SlidesarXivPDFCamera-readyHALDOICode
Cited by 7
  1. Correlation-Aware Reconstruction for Multivariate Time Series Anomaly Detection
    MJ Raihan, ZZ Darban, Y Liu, FD Salim…·arXiv preprint arXiv …·2026Scholar + S2
  2. ST-CCGN: Spatiotemporal Co-Clustering Graph Network for Multivariate Time-Series Anomaly Detection in Industrial Control Systems
    Ya-Wen Xue, Ze-Yu Yang, David K. Y. Yau, Rui-Long Deng·IEEE Transactions on Industrial Cyber-Physical Systems·2026Scholar + S2
  3. Evaluating univariate time series anomaly detection: Results and practical guidelines from the UCR Anomaly Archive
    D Cacciagrano, F Corradini, A Mehmeti…·Intelligent Systems with …·2026Scholar
  4. Exploring Zero-Shot Foundation Models for Multivariate Time Series Anomaly Detection
    M. Uray, S. Messineo, Roland Kwitt, Stefan Huber·arXiv.org·2026S2
  5. Modeling Normal Is All You Need: Joint Latent Clustering for Anomaly Detection in Multimodal Cyber-Physical Systems
    Alexander Apartsin, Y. Aperstein·arXiv.org·2026S2
  6. Lightweight CNN-Based Anomaly Detection for High Voltage Converter Modulators in the Spallation Neutron Source
    Alberto D. Cencillo, Leonardo Concepción, J. Luengo, I. Triguero·arXiv.org·2026S2
  7. Contaminated by Construction: Training Contamination and Sampling Granularity Decide Which Anomaly Detector Wins
    H Tabasi, G Gupta·Available at SSRN 7415960·2026Scholar
Google Scholar, Semantic Scholar and OpenAlex combined, duplicates merged · updated 23 Oct 2026