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Recent advancements in time-series anomaly detection have relied on deep learning models to handle the diverse behaviors of time-series data.
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USAD: UnSupervised Anomaly Detection on Multivariate Time Series
Audibert, J.; Michiardi, P.; Guyard, F.; Marti, S.; and Zuluaga, M. A. 2020 · 2020
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Timeseries anomaly detection using temporal hierarchical one-class network
Shen, L.; Li, Z.; and Kwok, J. 2020 · 2020
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Practical approach to asynchronous multivariate time series anomaly detection and localization
Abdulaal, A.; Liu, Z.; and Lancewicki, T. 2021 · 2021
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Learning graph structures with transformer for multivariate time-series anomaly detection in IoT
Chen, Z.; Chen, D.; Zhang, X.; Yuan, Z.; and Cheng, X. 2021 · 2021
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Graph neural network-based anomaly detection in multivariate time series
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Timely detection and mitigation of stealthy DDoS attacks via IoT networks
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Tuli, S.; Casale, G.; and Jennings, N. R. 2022 · 2022
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Timesnet: Temporal 2d-variation modeling for general time series analysis
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An innovative deep anomaly detection of building energy consumption using energy time-series images
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A decoder-only foundation model for time-series forecasting
Das, A.; Kong, W.; Sen, R.; and Zhou, Y. 2023 · 2023
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Is it worth it? Comparing six deep and classical methods for unsupervised anomaly detection in time series
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Llama 2: Open foundation and fine-tuned chat models
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ECGGAN: A Framework for Effective and Interpretable Electrocardiogram Anomaly Detection
Wang, H.; Luo, Z.; Yip, J. W.; Ye, C.; and Zhang, M. 2023 · 2023
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One fits all: Power general time series analysis by pretrained lm
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Memto: Memory-guided transformer for multivariate time series anomaly detection
Song, J.; Kim, K.; Oh, J.; and Cho, S. 2024 · 2024
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