108 citations · 116 across the 31 of their papers we have counts for
21 papers · 1 filter
AdaKoop: Efficient Modeling of Nonlinear Dynamics from Nonstationary Data Streams with Koopman Operator Regression
Naoki Chihara, Ren Fujiwara, Yasuko Matsubara +1
Real-time data analysis requires the ability to accurately and adaptively address nonlinear dynamics in a nonstationary data stream while preserving computational efficiency. Howev…
Modeling Dynamic Mixtures of Time-Delay Systems from Streaming Time Series
Ren Fujiwara, Yasuko Matsubara, Yasushi Sakurai
This research addresses the problem of adaptive modeling in time-series data streams with clear input-output relationships. This problem is challenging because rapid system changes…
When to Retrain after Drift: A Data-Only Test of Post-Drift Data Size Sufficiency
Ren Fujiwara, Yasuko Matsubara, Yasushi Sakurai
Sudden concept drift makes previously trained predictors unreliable, yet deciding when to retrain and what post-drift data size is sufficient is rarely addressed. We propose CALIPE…
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…
TIFO: Time-Invariant Frequency Operator for Stationarity-Aware Representation Learning in Time Series
Xihao Piao, Zheng Chen, Lingwei Zhu +3
Nonstationary time series forecasting suffers from the distribution shift issue due to the different distributions that produce the training and test data. Existing methods attempt…