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Detecting anomalies in real-world multivariate time series data is challenging due to complex temporal dependencies and inter-variable correlations.
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Memory matching networks for one-shot image recognition
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Generative adversarial networks
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2020
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Memory-augmented dense predictive coding for video representation learning
Tengda Han, Weidi Xie, and Andrew Zisserman · 2020
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Hyunjong Park, Jongyoun Noh, and Bumsub Ham · 2020
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Lifeng Shen, Zhuocong Li, and James Kwok · 2020
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Practical approach to asynchronous multivariate time series anomaly detection and localization
Ahmed Abdulaal, Zhuanghua Liu, and Tomer Lancewicki · 2021
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A review on outlier/anomaly detection in time series data
Ane Blázquez-García, Angel Conde, Usue Mori, and Jose A Lozano · 2021
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Deep autoencoding gaussian mixture model for unsupervised anomaly detection
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Memorizing normality to detect anomaly: Memory-augmented deep autoencoder for unsupervised anomaly detection
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Mad-gan: Multivariate anomaly detection for time series data with generative adversarial networks
Dan Li, Dacheng Chen, Baihong Jin, Lei Shi, Jonathan Goh, and See-Kiong Ng · 2019
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Mad-gan: Multivariate anomaly detection for time series data with generative adversarial networks
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Multivariate time series anomaly detection and interpretation using hierarchical inter-metric and temporal embedding
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A hybrid video anomaly detection framework via memory-augmented flow reconstruction and flow-guided frame prediction
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Time series anomaly detection with multiresolution ensemble decoding
Lifeng Shen, Zhongzhong Yu, Qianli Ma, and James T Kwok · 2021
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Anomaly transformer: Time series anomaly detection with association discrepancy
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Informer: Beyond efficient transformer for long sequence time-series forecasting
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Unsupervised model selection for time-series anomaly detection
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Variational transformer-based anomaly detection approach for multivariate time series
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