5 citations · 6 across the 8 of their papers we have counts for
8 papers
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…
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…
D-Tracker: Modeling Interest Diffusion in Social Activity Tensor Data Streams
Shingo Higashiguchi, Yasuko Matsubara, Koki Kawabata +2
Large quantities of social activity data, such as weekly web search volumes and the number of new infections with infectious diseases, reflect peoples' interests and activities. It…
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, . .…
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…
Fast and Multi-aspect Mining of Complex Time-stamped Event Streams
Kota Nakamura, Yasuko Matsubara, Koki Kawabata +3
Given a huge, online stream of time-evolving events with multiple attributes, such as online shopping logs: (item, price, brand, time), and local mobility activities: (pick-up and…
SSMF: Shifting Seasonal Matrix Factorization
Koki Kawabata, Siddharth Bhatia, Rui Liu +2
Given taxi-ride counts information between departure and destination locations, how can we forecast their future demands? In general, given a data stream of events with seasonal pa…