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The current state of machine learning scholarship in Timeseries Anomaly Detection (TAD) is plagued by the persistent use of flawed evaluation metrics, inconsistent benchmarking practices, and a lack of proper justification for the choices made in novel deep learning-based model designs.
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An overview of anomaly detection techniques: Existing solutions and latest technological trends
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Anomaly detection in medical wireless sensor networks using svm and linear regression models
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Swat: a water treatment testbed for research and training on ics security
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Guillame-Bert, M. and Dubrawski, A · 2017
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Isolation-based anomaly detection using nearest-neighbor ensembles: inne
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A multimodal anomaly detector for robot-assisted feeding using an lstm-based variational autoencoder
Park, D., Hoshi, Y., and Kemp, C · 2018
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Precision and recall for time series
Tatbul, N., Lee, T. J., Zdonik, S., Alam, M., and Gottschlich, J · 2018
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Unsupervised anomaly detection via variational auto-encoder for seasonal kpis in web applications
Xu, H., Chen, W., Zhao, N., Li, Z., Bu, J., Li, Z., Liu, Y., Zhao, Y., Pei, D., Feng, Y., Chen, J., Wang, Z., and Qiao, H · 2018
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Deep autoencoding gaussian mixture model for unsupervised anomaly detection
Zong, B., Song, Q., Min, M. R., Cheng, W., Lumezanu, C., Cho, D., and Chen, H · 2018
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MAD-GAN: Multivariate anomaly detection for time series data with generative adversarial networks
Li, D., Chen, D., Jin, B., Shi, L., Goh, J., and Ng, S.-K · 2019
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A deep neural network for unsupervised anomaly detection and diagnosis in multivariate time series data
Zhang, C., Song, D., Chen, Y., Feng, X., Lumezanu, C., Cheng, W., Ni, J., Zong, B., Chen, H., and Chawla, N. V · 2019
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Graph neural network-based anomaly detection in multivariate time series
Deng, A. and Hooi, B · 2021
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Time series anomaly detection for cyber-physical systems via neural system identification and bayesian filtering
Feng, C. and Tian, P · 2021
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MLP-mixer: An all-MLP architecture for vision
Tolstikhin, I., Houlsby, N., Kolesnikov, A., Beyer, L., Zhai, X., Unterthiner, T., Yung, J., Steiner, A. P., Keysers, D., Uszkoreit, J., Lucic, M., and Dosovitskiy, A · 2021
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Do deep neural networks contribute to multivariate time series anomaly detection?
Audibert, J., Michiardi, P., Guyard, F., Marti, S., and Zuluaga, M. A · 2022
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Towards a rigorous evaluation of time-series anomaly detection
Kim, S., Choi, K., Choi, H.-S., Lee, B., and Yoon, S · 2022
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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
Cited alongside, same era.
Graphan: Graph-based subsequence anomaly detection
Boniol, P., Palpanas, T., Meftah, M., and Remy, E · 2020
Cited alongside, same era.
Merlin: Parameter-free discovery of arbitrary length anomalies in massive time series archives
Nakamura, T., Imamura, M., Mercer, R., and Keogh, E · 2020
Cited alongside, same era.
Timeseries anomaly detection using temporal hierarchical one-class network
Shen, L., Li, Z., and Kwok, J · 2020
Cited alongside, same era.
Real-time distance-based outlier detection in data streams
Tran, L., Mun, M. Y., and Shahabi, C · 2020
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Multivariate time-series anomaly detection via graph attention network
Zhao, H., Wang, Y., Duan, J., Huang, C., Cao, D., Tong, Y., Xu, B., Bai, J., Tong, J., and Zhang, Q · 2020
Cited alongside, same era.
A review on outlier/anomaly detection in time series data
Blázquez-García, A., Conde, A., Mori, U., and Lozano, J. A · 2021
Cited alongside, same era.
Paparrizos, J., Kang, Y., Boniol, P., Tsay, R. S., Palpanas, T., and Franklin, M. J · 2022
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Anomaly detection in time series: A comprehensive evaluation
Schmidl, S., Wenig, P., and Papenbrock, T · 2022
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TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data
Tuli, S., Casale, G., and Jennings, N. R · 2022
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Current time series anomaly detection benchmarks are flawed and are creating the illusion of progress (extended abstract)
Wu, R. and Keogh, E. J · 2022
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Anomaly transformer: Time series anomaly detection with association discrepancy
Xu, J., Wu, H., Wang, J., and Long, M · 2022
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Prototype-oriented unsupervised anomaly detection for multivariate time series
Li, Y., Chen, W., Chen, B., Wang, D., Tian, L., and Zhou, M · 2023
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TimeseAD: Benchmarking deep multivariate time-series anomaly detection
Wagner, D., Michels, T., Schulz, F. C., Nair, A., Rudolph, M., and Kloft, M · 2023
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One fits all: Power general time series analysis by pretrained lm
Zhou, T., Niu, P., Sun, L., Jin, R., et al · 2023
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Problems with time series anomaly detection
Eamonn Keogh · 2024
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