Multivariate Time Series Anomaly Detection: Fancy Algorithms and Flawed Evaluation Methodology

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Ahmed, C.M., Palleti, V.R., Mathur, A.P.: Wadi: a water distribution testbed for research in the design of secure cyber physical systems. In: Proceedings of the 3rd international workshop on cyber-physical systems for smart water networks. pp. 25–28 (2017)

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Hundman, K., Constantinou, V., Laporte, C., Colwell, I., Soderstrom, T.: Detecting spacecraft anomalies using lstms and nonparametric dynamic thresholding. In: Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining. pp. 387–395 (2018)

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Zong, B., Song, Q., Min, M.R., Cheng, W., Lumezanu, C., Cho, D., Chen, H.: Deep autoencoding gaussian mixture model for unsupervised anomaly detection. In: International conference on learning representations (2018)

2018

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Su, Y., Zhao, Y., Niu, C., Liu, R., Sun, W., Pei, D.: Robust anomaly detection for multivariate time series through stochastic recurrent neural network. In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining. pp. 2828–2837 (2019)

2019

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Zhang, C., Song, D., Chen, Y., Feng, X., Lumezanu, C., Cheng, W., Ni, J., Zong, B., Chen, H., Chawla, N.V.: A deep neural network for unsupervised anomaly detection and diagnosis in multivariate time series data. In: Proceedings of the AAAI conference on artificial intelligence. vol. 33, pp. 1409–1416 (2019)

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Audibert, J., Michiardi, P., Guyard, F., Marti, S., Zuluaga, M.A.: Usad: Unsupervised anomaly detection on multivariate time series. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. pp. 3395–3404 (2020)

2020

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Abdulaal, A., Liu, Z., Lancewicki, T.: Practical approach to asynchronous multivariate time series anomaly detection and localization. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. pp. 2485–2494 (2021)

2021

Cited alongside, same era.

Chen, X., Deng, L., Huang, F., Zhang, C., Zhang, Z., Zhao, Y., Zheng, K.: Daemon: Unsupervised anomaly detection and interpretation for multivariate time series. In: 2021 IEEE 37th International Conference on Data Engineering (ICDE). pp. 2225–2230. IEEE (2021)

2021

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Chen, Z., Chen, D., Zhang, X., Yuan, Z., Cheng, X.: Learning graph structures with transformer for multivariate time series anomaly detection in iot. IEEE Internet of Things Journal (2021)

2021

Cited alongside, same era.

Deng, A., Hooi, B.: Graph neural network-based anomaly detection in multivariate time series. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 35, pp. 4027–4035 (2021)

2021

Han, S., Woo, S.S.: Learning sparse latent graph representations for anomaly detection in multivariate time series. In: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. pp. 2977–2986 (2022)

2022

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Kim, S., Choi, K., Choi, H.S., Lee, B., Yoon, S.: Towards a rigorous evaluation of time-series anomaly detection. In: Proceedings of the AAAI Conference on Artificial Intelligence. pp. 7194–7201 (2022)

2022

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Pan, J., Ji, W., Zhong, B., Wang, P., Wang, X., Chen, J.: Duma: Dual mask for multivariate time series anomaly detection. IEEE Sensors Journal (2022)

2022

Later among the works it cites.

Tuli, S., Casale, G., Jennings, N.R.: TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data. Proceedings of VLDB 15

2022

Later among the works it cites.