6 papers
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…
Multi-Aspect Mining and Anomaly Detection for Heterogeneous Tensor Streams
Soshi Kakio, Yasuko Matsubara, Ren Fujiwara +1
Analysis and anomaly detection in event tensor streams consisting of timestamps and multiple attributes - such as communication logs(time, IP address, packet length)- are essential…
Modeling Time-evolving Causality over Data Streams
Naoki Chihara, Yasuko Matsubara, Ren Fujiwara +1
Given an extensive, semi-infinite collection of multivariate coevolving data sequences (e.g., sensor/web activity streams) whose observations influence each other, how can we disco…
Modeling Latent Non-Linear Dynamical System over Time Series
Ren Fujiwara, Yasuko Matsubara, Yasushi Sakurai
We study the problem of modeling a non-linear dynamical system when given a time series by deriving equations directly from the data. Despite the fact that time series data are giv…