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\n  \n article\n \n \n (2)\n \n \n
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\n \n\n \n \n \n \n \n \n An interactive threshold-setting procedure for improved multivariate anomaly detection in time series.\n \n \n \n \n\n\n \n Lundström, A.; O’Nils, M.; and Qureshi, F. Z.\n\n\n \n\n\n\n IEEE Access,11pp. 2023.\n \n\n\n\n
\n\n\n\n \n \n \"An paper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
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@ARTICLE{23-ieee-access-j,\n  author={Lundström, Adam and O’Nils, Mattias and Qureshi, Faisal Z.},\n  journal={IEEE Access}, \n  title={An interactive threshold-setting procedure for improved multivariate anomaly detection in time series}, \n  year={2023},\n  volume={},\n  number={},\n  pages={11pp},\n  abstract={Anomaly detection in multivariate time series is valuable for many applications. In this context, unsupervised and semi-supervised deep learning methods that estimate how normal a new observation is have shown promising results on benchmark datasets. These methods are dependent on a threshold that determines which points should be regarded as anomalous and not be anomalous. However, finding the optimal threshold is not easy since no information about the ground truth is known in advance, which implies that there are limitations to automatic threshold-setting methods available today. An alternative is to utilize the expertise of users that can interact in a threshold-setting procedure, but for this to be practically feasible, the method needs to be both accurate and efficient in relation to the state-of-the-art automatic methods. Therefore, this study develops an interactive threshold-setting schema and examines to what extent it can outperform the current state-of-the-art automatic threshold-setting methods. The result of the study strongly indicates that the suggested method with little effort can provide higher accuracy than the automatic threshold-setting methods on a general basis.},\n  doi={10.1109/ACCESS.2023.3310653},\n  ISSN={2169-3536},\n  month={},\n  keywords = {anomaly-scoring},\n  url_Paper = {pubs/23-ieee-access-j.pdf}\n}\n\n
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\n Anomaly detection in multivariate time series is valuable for many applications. In this context, unsupervised and semi-supervised deep learning methods that estimate how normal a new observation is have shown promising results on benchmark datasets. These methods are dependent on a threshold that determines which points should be regarded as anomalous and not be anomalous. However, finding the optimal threshold is not easy since no information about the ground truth is known in advance, which implies that there are limitations to automatic threshold-setting methods available today. An alternative is to utilize the expertise of users that can interact in a threshold-setting procedure, but for this to be practically feasible, the method needs to be both accurate and efficient in relation to the state-of-the-art automatic methods. Therefore, this study develops an interactive threshold-setting schema and examines to what extent it can outperform the current state-of-the-art automatic threshold-setting methods. The result of the study strongly indicates that the suggested method with little effort can provide higher accuracy than the automatic threshold-setting methods on a general basis.\n
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\n \n\n \n \n \n \n \n \n Improving deep learning based anomaly detection on multivariate time series through separated anomaly scoring.\n \n \n \n \n\n\n \n Lundstrom, A.; O'Nils, M.; Qureshi, F. Z.; and Jantsch, A.\n\n\n \n\n\n\n IEEE Access, 10: 11pp. Oct 2022.\n \n\n\n\n
\n\n\n\n \n \n \"ImprovingPaper\n  \n \n \n \"Improving paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
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@Article{22-ieee-access-j,\n  author =       {Lundstrom, Adam and O'Nils, Mattias and Qureshi, Faisal Z. and Jantsch, Axel},\n  title =        {Improving deep learning based anomaly detection on multivariate time series through separated anomaly scoring},\n  journal =      {IEEE Access},\n  year =         {2022},\n  volume =    {10},\n  number =    {},\n  pages =     {11pp},\n  month =     {Oct},\n  url = {10.1109/ACCESS.2022.3213038},\n  keywords = {anomaly-scoring},\n  url_Paper = {pubs/22-ieee-access-j.pdf}\n}\n\n
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