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CLOSURE: Cross-Channel Closure for Multivariate Time Series Anomaly Detection

Paper recorded by Signals 4 on 2026-10-26 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-26 on arXiv · recorded by Signals 4 on 2026-10-27

Category: cs.AI · 人工智能 · first seen 2026-10-27

Abstract

Multivariate time series produced by industrial installations and server clusters are under continuous monitoring, and anomaly detection is the first line of defense before a local fault escalates into a system-wide failure. In the absence of labels, the dominant paradigm trains a reconstruction model on normal data and scores the residual to reach a verdict. Recent work has steadily enriched the

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#156 most recent of 960 cs.AI papers we have recorded · ↑ newer: EEGaze: Multimodal Attention Prediction in Visual Search via EEG-Based · ↓ older: Multi-Objective Reinforcement Learning Using Routed Ensembles and Traj
Cite this page: CLOSURE: Cross-Channel Closure for Multivariate Time Series Anomaly Detection: the #156 most recent of 960 cs.AI papers we have recorded (as of 2026-10-26). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/closure-cross-channel-closure-for-multivariate-time-series-anomaly-detection.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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