CoAD unifies outlier exposure classification and masked autoencoder reconstruction in a cooperative loop to detect subtle and prolonged time series anomalies.
Tsay, Themis Palpanas, and Michael J
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
TPA-AD generates boundary-near pseudo-anomalies via reconstruction, applies contrastive learning, and uses KNN to score anomalies in bearing time series with only normal training samples.
citing papers explorer
-
Bridging Classification and Reconstruction: Cooperative Time Series Anomaly Detection
CoAD unifies outlier exposure classification and masked autoencoder reconstruction in a cooperative loop to detect subtle and prolonged time series anomalies.
-
TPA-AD: A Two-Stage Pseudo Anomaly-Guided Method for Bearing Time-Series Anomaly Detection
TPA-AD generates boundary-near pseudo-anomalies via reconstruction, applies contrastive learning, and uses KNN to score anomalies in bearing time series with only normal training samples.