Online Event Detection in Streaming Time Series: Novel Metrics and Practical Insights
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| Titel: | Online Event Detection in Streaming Time Series: Novel Metrics and Practical Insights |
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| Autoren: | Lima, Janio, Tavares, Lucas, Pacitti, Esther, Ferreira, João Eduardo, Santos, Ismael, Guimaraes Siqueira, Isabela, Carvalho, Diego, Porto, Fabio, Coutinho, Rafaelli, Ogasawara, Eduardo |
| Weitere Verfasser: | Valduriez, Patrick, International Neural Network Society |
| Quelle: | 2024 International Joint Conference on Neural Networks (IJCNN). :1-8 |
| Verlagsinformationen: | IEEE, 2024. |
| Publikationsjahr: | 2024 |
| Schlagwörter: | Time series, Streaming data, Event detection, [INFO] Computer Science [cs] |
| Beschreibung: | Online event detection in streaming time series is a critical task with applications across various domains. For example, the right-on-time event detection for control systems is a key for correctly addressing the issues related to the events. However, events may not be identified right after their occurrence. Depending on the monitoring solution, a time difference may exist between the event’s occurrence and detection. This problem raises research questions regarding the study of such a temporal gap. The paper introduces novel metrics (detection probability and detection lag) to address these questions. It explores the impact of configurable batches on detection performance. The experimental evaluation of diverse datasets reveals nuanced insights into the interplay between batch parameters, detection accuracy, and computational performance. |
| Publikationsart: | Article Conference object |
| Dateibeschreibung: | application/pdf |
| DOI: | 10.1109/ijcnn60899.2024.10650809 |
| Zugangs-URL: | https://hal-lirmm.ccsd.cnrs.fr/lirmm-04674128v1/document https://hal-lirmm.ccsd.cnrs.fr/lirmm-04674128v1 https://doi.org/10.1109/ijcnn60899.2024.10650809 |
| Rights: | STM Policy #29 |
| Dokumentencode: | edsair.doi.dedup.....97bf5f08d1341a2449dd6eee44547b70 |
| Datenbank: | OpenAIRE |
Nájsť tento článok vo Web of Science | Abstract: | Online event detection in streaming time series is a critical task with applications across various domains. For example, the right-on-time event detection for control systems is a key for correctly addressing the issues related to the events. However, events may not be identified right after their occurrence. Depending on the monitoring solution, a time difference may exist between the event’s occurrence and detection. This problem raises research questions regarding the study of such a temporal gap. The paper introduces novel metrics (detection probability and detection lag) to address these questions. It explores the impact of configurable batches on detection performance. The experimental evaluation of diverse datasets reveals nuanced insights into the interplay between batch parameters, detection accuracy, and computational performance. |
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| DOI: | 10.1109/ijcnn60899.2024.10650809 |