5 papers · 1 filter
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…
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…
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…
Mining of Switching Sparse Networks for Missing Value Imputation in Multivariate Time Series
Kohei Obata, Koki Kawabata, Yasuko Matsubara +1
Multivariate time series data suffer from the problem of missing values, which hinders the application of many analytical methods. To achieve the accurate imputation of these missi…
Dynamic Multi-Network Mining of Tensor Time Series
Kohei Obata, Koki Kawabata, Yasuko Matsubara +1
Subsequence clustering of time series is an essential task in data mining, and interpreting the resulting clusters is also crucial since we generally do not have prior knowledge of…