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Time series anomaly detection (TSAD) is becoming increasingly vital due to the rapid growth of time series data across various sectors.
Deep Learning for Anomaly Detection: A Survey
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Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applications
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A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Data. In AAAI Conference on Artificial Intelligence
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Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection. In International Conference on Learning Representations
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A Systematic Framework to Generate Invariants for Anomaly Detection in Industrial Control Systems
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MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks. In Artificial Neural Networks and Machine Learning – ICANN 2019: Text and Time Series: 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17–19, 2019, Proceedings, Part IV . Springer-Verlag, Berlin, Heidelberg, 703–716
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Robust Anomaly Detection for Multivariate Time Series through Stochastic Recurrent Neural Network
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USAD: UnSupervised Anomaly Detection on Multivariate Time Series. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD ’20) . Association for Computing Machinery, New York, NY, USA, 3395–3404
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Energy-Efficient and QoS-Optimized Adaptive Task Scheduling and Management in Clouds
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Multivariate Time-Series Anomaly Detection via Graph Attention Network. In 2020 IEEE International Conference on Data Mining (ICDM) . 841–850
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Cited alongside, same era.
A Review on Outlier/Anomaly Detection in Time Series Data
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Unsupervised and scalable subsequence anomaly detection in large data series
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Image Classification of Time Series Based on Deep Convolutional Neural Network. In 2021 40th Chinese Control Conference (CCC) . 8488–8491
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Deep Learning for Anomaly Detection in Time-Series Data: Review, Analysis, and Guidelines
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Unsupervised Deep Learning for IoT Time Series
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Recent Advances in Natural Language Processing via Large Pre-trained Language Models: A Survey
Bonan Min, Hayley Ross, Elior Sulem, Amir Pouran Ben Veyseh, Thien Huu Nguyen, Oscar Sainz, Eneko Agirre, Ilana Heintz, and Dan Roth. 2023 · 2023
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A Comprehensive Overview of Large Language Models
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Is It Worth It? Comparing Six Deep and Classical Methods for Unsupervised Anomaly Detection in Time Series
Ferdinand Rewicki, Joachim Denzler, and Julia Niebling. 2023 · 2023
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TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis. In The Eleventh International Conference on Learning Representations
Haixu Wu, Tengge Hu, Yong Liu, Hang Zhou, Jianmin Wang, and Mingsheng Long. 2023 · 2023
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Graph Neural Network-Based Anomaly Detection in Multivariate Time Series. In AAAI Conference on Artificial Intelligence
Ailin Deng and Bryan Hooi. 2021 · 2021
Cited alongside, same era.
Exathlon: a benchmark for explainable anomaly detection over time series
Vincent Jacob, Fei Song, Arnaud Stiegler, Bijan Rad, Yanlei Diao, and Nesime Tatbul. 2021 · 2021
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Detection and Classification of Anomalies in Large Datasets on the Basis of Information Granules
Adam Kiersztyn, Paweł Karczmarek, Krystyna Kiersztyn, and Witold Pedrycz. 2022 · 2021
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Towards a Rigorous Evaluation of Time-series Anomaly Detection. In AAAI Conference on Artificial Intelligence
Siwon Kim, Kukjin Choi, Hyun-Soo Choi, Byunghan Lee, and Sungroh Yoon. 2021 · 2021
Cited alongside, same era.
Revisiting Time Series Outlier Detection: Definitions and Benchmarks. In Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track
Kwei-Herng Lai, Daochen Zha, Junjie Xu, Yue Zhao, Guanchu Wang, and Xia Hu. 2021 · 2021
Cited alongside, same era.
Anomaly Detection and Classification in Multispectral Time Series Based on Hidden Markov Models
Kareth M. León-López, Florian Mouret, Henry Arguello, and Jean-Yves Tourneret. 2022 · 2021
Cited alongside, same era.
Current time series anomaly detection benchmarks are flawed and are creating the illusion of progress
Renjie Wu and Eamonn Keogh. 2021 · 2021
Cited alongside, same era.
Later among the works it cites.
Deep Isolation Forest for Anomaly Detection
Hongzuo Xu, Guansong Pang, Yijie Wang, and Yongjun Wang. 2023 · 2023
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Large language models can be zero-shot anomaly detectors for time series?
Sarah Alnegheimish, Linh Nguyen, Laure Berti-Equille, and Kalyan Veeramachaneni. 2024 · 2024
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A Survey on Evaluation of Large Language Models
Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Linyi Yang, Kaijie Zhu, Hao Chen, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, Wei Ye, Yue Zhang, Yi Chang, Philip S. Yu, Qiang Yang, and Xing Xie. 2024 · 2024
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LARA: A Light and Anti-overfitting Retraining Approach for Unsupervised Time Series Anomaly Detection. In Proceedings of the ACM Web Conference 2024 (WWW ’24) . Association for Computing Machinery, New York, NY, USA, 4138–4149
Feiyi Chen, Zhen Qin, Mengchu Zhou, Yingying Zhang, Shuiguang Deng, Lunting Fan, Guansong Pang, and Qingsong Wen. 2024 · 2024
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Large Language Models Are Zero-Shot Time Series Forecasters
Nate Gruver, Marc Finzi, Shikai Qiu, and Andrew Gordon Wilson. 2024 · 2024
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UrbanGPT: Spatio-Temporal Large Language Models. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD ’24) . Association for Computing Machinery, New York, NY, USA, 5351–5362
Zhonghang Li, Lianghao Xia, Jiabin Tang, Yong Xu, Lei Shi, Long Xia, Dawei Yin, and Chao Huang. 2024 · 2024
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LSTPrompt: Large Language Models as Zero-Shot Time Series Forecasters by Long-Short-Term Prompting
Haoxin Liu, Zhiyuan Zhao, Jindong Wang, Harshavardhan Kamarthi, and B. Aditya Prakash. 2024 · 2024
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Language Models Still Struggle to Zero-shot Reason about Time Series
Mike A. Merrill, Mingtian Tan, Vinayak Gupta, Tom Hartvigsen, and Tim Althoff. 2024 · 2024
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Breaking the Time-Frequency Granularity Discrepancy in Time-Series Anomaly Detection. In Proceedings of the ACM Web Conference 2024 (WWW ’24) . Association for Computing Machinery, New York, NY, USA, 4204–4215
Youngeun Nam, Susik Yoon, Yooju Shin, Minyoung Bae, Hwanjun Song, Jae-Gil Lee, and Byung Suk Lee. 2024 · 2024
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From anomaly detection to classification with graph attention and transformer for multivariate time series
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Towards Cross-Table Masked Pretraining for Web Data Mining. In Proceedings of the ACM Web Conference 2024 (WWW ’24) . Association for Computing Machinery, New York, NY, USA, 4449–4459
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Supervised Fine-Tuning for Unsupervised KPI Anomaly Detection for Mobile Web Systems. In Proceedings of the ACM Web Conference 2024 (WWW ’24) . Association for Computing Machinery, New York, NY, USA, 2859–2869
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The area under the precision-recall curve as a performance metric for rare binary events
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