3 papers
cs.LG2026
Disentangled Mode-Specific Representations for Tensor Time Series via Contrastive Learning
Kohei Obata, Taichi Murayama, Zheng Chen +2
Multi-mode tensor time series (TTS) can be found in many domains, such as search engines and environmental monitoring systems. Learning representations of a TTS benefits various ap…
cs.LG2026
Selective Denoising Diffusion Model for Time Series Anomaly Detection
Kohei Obata, Zheng Chen, Yasuko Matsubara +2
Time series anomaly detection (TSAD) has been an important area of research for decades, with reconstruction-based methods, mostly based on generative models, gaining popularity an…
cs.LG2025
Robust and Explainable Detector of Time Series Anomaly via Augmenting Multiclass Pseudo-Anomalies
Kohei Obata, Yasuko Matsubara, Yasushi Sakurai
Unsupervised anomaly detection in time series has been a pivotal research area for decades. Current mainstream approaches focus on learning normality, on the assumption that all or…