output
20162026
most citedFredformer: Frequency Debiased Transformer for Time Series Forecasting

108 citations

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7 papers · 1 filter

cs.LG2026

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…

cs.LG2026

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…

cs.LG2025

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, . .…

cs.LG2025★ 2 cited

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…

cs.LG2025★ 1 cited

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…

cs.LG2024★ 108 cited

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…