Using Multi-Scale Convolution Fusion and Memory-Augmented Adversarial Autoencoder to Detect Diverse Anomalies in Multivariate Time Series
Author:
Affiliation:
1. College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology,Jinzhong,China,030600
Funder
National Key Research and Development Program of China
Publisher
Tsinghua University Press
Link
http://xplorestaging.ieee.org/ielx8/5971803/10676339/10676353.pdf?arnumber=10676353
Reference35 articles.
1. A Semi-Supervised VAE Based Active Anomaly Detection Framework in Multivariate Time Series for Online Systems
2. Forecasting Error Pattern-Based Anomaly Detection in Multivariate Time Series
3. Unsupervised Anomaly Detection for Time Series Data of Spacecraft Using Multi-Task Learning
4. A Review on Outlier/Anomaly Detection in Time Series Data
5. Temporal convolutional autoencoder for unsupervised anomaly detection in time series
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