108 citations · 116 across the 30 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…
Interpretable Dynamic Network Modeling of Tensor Time Series via Kronecker Time-Varying Graphical Lasso
Shingo Higashiguchi, Koki Kawabata, Yasuko Matsubara +1
With the rapid development of web services, large amounts of time series data are generated and accumulated across various domains such as finance, healthcare, and online platforms…
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…
Fast Mining and Dynamic Time-to-Event Prediction over Multi-sensor Data Streams
Kota Nakamura, Koki Kawabata, Yasuko Matsubara +1
Given real-time sensor data streams obtained from machines, how can we continuously predict when a machine failure will occur? This work aims to continuously forecast the timing of…
EvoBrain: Dynamic Multi-Channel EEG Graph Modeling for Time-Evolving Brain Networks
Rikuto Kotoge, Zheng Chen, Tasuku Kimura +4
Dynamic GNNs, which integrate temporal and spatial features in Electroencephalography (EEG) data, have shown great potential in automating seizure detection. However, fully capturi…
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…
CyberCScope: Mining Skewed Tensor Streams and Online Anomaly Detection in Cybersecurity Systems
Kota Nakamura, Koki Kawabata, Shungo Tanaka +2
Cybersecurity systems are continuously producing a huge number of time-stamped events in the form of high-order tensors, such as {count; time, port, flow duration, packet size, . .…
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…
ExPath: Targeted Pathway Inference for Biological Knowledge Bases via Graph Learning and Explanation
Rikuto Kotoge, Ziwei Yang, Zheng Chen +4
Retrieving targeted pathways in biological knowledge bases, particularly when incorporating wet-lab experimental data, remains a challenging task and often requires downstream anal…
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…
Long-Term EEG Partitioning for Seizure Onset Detection
Zheng Chen, Yasuko Matsubara, Yasushi Sakurai +1
Deep learning models have recently shown great success in classifying epileptic patients using EEG recordings. Unfortunately, classification-based methods lack a sound mechanism to…
SplitSEE: A Splittable Self-supervised Framework for Single-Channel EEG Representation Learning
Rikuto Kotoge, Zheng Chen, Tasuku Kimura +4
While end-to-end multi-channel electroencephalography (EEG) learning approaches have shown significant promise, their applicability is often constrained in neurological diagnostics…
GeSubNet: Gene Interaction Inference for Disease Subtype Network Generation
Ziwei Yang, Zheng Chen, Xin Liu +5
Retrieving gene functional networks from knowledge databases presents a challenge due to the mismatch between disease networks and subtype-specific variations. Current solutions, i…
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…
Fredformer: Frequency Debiased Transformer for Time Series Forecasting
Xihao Piao, Zheng Chen, Taichi Murayama +2
The Transformer model has shown leading performance in time series forecasting. Nevertheless, in some complex scenarios, it tends to learn low-frequency features in the data and ov…
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…