\begin{thebibliography}{15}
\providecommand{\natexlab}[1]{#1}
\providecommand{\url}[1]{\texttt{#1}}
\expandafter\ifx\csname urlstyle\endcsname\relax
  \providecommand{\doi}[1]{doi: #1}\else
  \providecommand{\doi}{doi: \begingroup \urlstyle{rm}\Url}\fi

\bibitem[Abdulaal et~al.(2021)Abdulaal, Liu, and Lancewicki]{abdulaal2021psm}
Ahmed Abdulaal, Zhuanghua Liu, and Tomer Lancewicki.
\newblock Practical approach to asynchronous multivariate time series anomaly
  detection and localization.
\newblock In \emph{Proceedings of the 27th ACM SIGKDD Conference on Knowledge
  Discovery \& Data Mining}, pages 2485--2494, 2021.

\bibitem[Audibert et~al.(2020)Audibert, Michiardi, Guyard, Marti, and
  Zuluaga]{audibert2020usad}
Julien Audibert, Pietro Michiardi, Fr{\'e}d{\'e}ric Guyard, S{\'e}bastien
  Marti, and Maria~A Zuluaga.
\newblock Usad: Unsupervised anomaly detection on multivariate time series.
\newblock In \emph{Proceedings of the 26th ACM SIGKDD International Conference
  on Knowledge Discovery \& Data Mining}, pages 3395--3404, 2020.

\bibitem[Chen et~al.(2020)Chen, Kornblith, Norber, and Hinton]{chen2020simclr}
Ting Chen, Simon Kornblith, Mohammad Norber, and Geoffrey Hinton.
\newblock A simple framework for contrastive learning of visual
  representations.
\newblock In \emph{International Conference on Machine Learning}, pages
  1597--1607. PMLR, 2020.

\bibitem[Hundman et~al.(2018)Hundman, Constantinou, Laporte, Colwell, and
  Soderstrom]{hundman2018detecting}
Kyle Hundman, Valentino Constantinou, Christopher Laporte, Ian Colwell, and Tom
  Soderstrom.
\newblock Detecting spacecraft anomalies using {LSTMs} and nonparametric
  dynamic thresholding.
\newblock In \emph{Proceedings of the 24th ACM SIGKDD International Conference
  on Knowledge Discovery \& Data Mining}, pages 387--395, 2018.
\newblock \doi{10.1145/3219819.3219845}.

\bibitem[Kim et~al.(2022)Kim, Choi, Choi, Lee, and Yoon]{kim2022towards}
Siwon Kim, Kukjin Choi, Hyun-Soo Choi, Byunghan Lee, and Sungroh Yoon.
\newblock Towards a rigorous evaluation of time-series anomaly detection.
\newblock In \emph{Proceedings of the AAAI Conference on Artificial
  Intelligence}, volume~36, pages 7062--7070, 2022.

\bibitem[Mathur and Tippenhauer(2016)]{mathur2016swat}
Aditya~P. Mathur and Nils~Ole Tippenhauer.
\newblock {SWaT}: A water treatment testbed for research and training on {ICS}
  security.
\newblock In \emph{International Workshop on Cyber-physical Systems for Smart
  Water Networks}, pages 31--36, 2016.

\bibitem[Park et~al.(2018)Park, Hoshi, and Kemp]{park2018multimodal}
Daehyung Park, Yuuna Hoshi, and Charles~C. Kemp.
\newblock A multimodal anomaly detector for robot-assisted feeding using an
  {LSTM}-based variational autoencoder.
\newblock \emph{IEEE Robotics and Automation Letters}, 3\penalty0 (3):\penalty0
  1544--1551, 2018.
\newblock \doi{10.1109/LRA.2018.2801475}.

\bibitem[Siffer et~al.(2017)Siffer, Fouque, Termier, and
  Largou{\"e}t]{siffer2017anomaly}
Alban Siffer, Pierre-Alain Fouque, Alexandre Termier, and Christine
  Largou{\"e}t.
\newblock Anomaly detection in streams with extreme value theory.
\newblock In \emph{Proceedings of the 23rd ACM SIGKDD International Conference
  on Knowledge Discovery and Data Mining}, pages 1067--1075, 2017.
\newblock \doi{10.1145/3097983.3098144}.

\bibitem[Su et~al.(2019)Su, Zhao, Niu, Liu, Sun, and Pei]{su2019omnianomaly}
Ya~Su, Youjian Zhao, Chenhao Niu, Rong Liu, Wei Sun, and Dan Pei.
\newblock Robust anomaly detection for multivariate time series through
  stochastic recurrent neural network.
\newblock In \emph{Proceedings of the 25th ACM SIGKDD International Conference
  on Knowledge Discovery \& Data Mining}, pages 2828--2837, 2019.

\bibitem[Tuli et~al.(2022)Tuli, Casale, and Jennings]{tuli2022tranad}
Shreshth Tuli, Giuliano Casale, and Nicholas~R. Jennings.
\newblock Tran{AD}: Deep transformer networks for anomaly detection in
  multivariate time series data.
\newblock \emph{Proceedings of the VLDB Endowment}, 15\penalty0 (6):\penalty0
  1201--1214, 2022.

\bibitem[Wu et~al.(2021)Wu, Xu, Wang, and Long]{wu2021autoformer}
Haixu Wu, Jiehui Xu, Jianmin Wang, and Mingsheng Long.
\newblock Autoformer: Decomposition transformers with auto-correlation for
  long-term series forecasting.
\newblock In \emph{Advances in Neural Information Processing Systems},
  volume~34, 2021.

\bibitem[Wu et~al.(2023)Wu, Hu, Liu, Zhou, Wang, and Long]{wu2023timesnet}
Haixu Wu, Tengge Hu, Yong Liu, Hang Zhou, Jianmin Wang, and Mingsheng Long.
\newblock Timesnet: Temporal 2d-variation modeling for general time series
  analysis.
\newblock In \emph{International Conference on Learning Representations}, 2023.

\bibitem[Xu et~al.(2022)Xu, Wu, Wang, and Long]{xu2022anomaly}
Jiehui Xu, Haixu Wu, Jianmin Wang, and Mingsheng Long.
\newblock Anomaly transformer: Time series anomaly detection with association
  discrepancy.
\newblock In \emph{International Conference on Learning Representations}, 2022.

\bibitem[Yang et~al.(2023)Yang, Zhang, Zhou, Wen, and Sun]{yang2023dcdetector}
Yiyuan Yang, Chaoli Zhang, Tian Zhou, Qingsong Wen, and Liang Sun.
\newblock Dcdetector: Dual attention contrastive representation learning for
  time series anomaly detection.
\newblock \emph{Proceedings of the 29th ACM SIGKDD Conference on Knowledge
  Discovery and Data Mining}, 2023.

\bibitem[Zhou et~al.(2022)Zhou, Ma, Wen, Wang, Sun, and Jin]{zhou2022fedformer}
Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin.
\newblock Fedformer: Frequency enhanced decomposed transformer for long-term
  series forecasting.
\newblock In \emph{International Conference on Machine Learning}, pages
  27268--27286. PMLR, 2022.

\end{thebibliography}
