Anomalies and Breakpoint Detection for a Dataset of Firefighters’ Operations During the COVID-19 Period in France
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Publisher
Springer International Publishing
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-04826-5_1
Reference18 articles.
1. Cerna, S., Arcolezi, H., Guyeux, C., Royer-Fey, G., Chevallier, C.: Machine learning-based forecasting of firemen ambulances’ turnaround time in hospitals, considering the COVID-19 impact. Appl. Soft Comput. 109, 107561 (2021)
2. Nahuis, S., Guyeux, C., Arcolezi, H., Couturier, R., Royer, G., Lotufo, A.: Long short-term memory for predicting firemen interventions. In: 2019 6th International Conference on Control, Decision and Information Technologies (CoDIT), pp. 1132–1137 (2019)
3. Couchot, J.F., Guyeux, C., Royer, G.: Anonymously forecasting the number and nature of firefighting operations. In: Proceedings of the 23rd International Database Applications & Engineering Symposium, pp. 1–8 (2019)
4. Guyeux, C., et al.: Firemen prediction by using neural networks: a real case study. In: Proceedings of SAI Intelligent Systems Conference, pp. 541–552 (2019)
5. Elias Mallouhy, R., Guyeux, C., Abou Jaoude, C., Makhoul, A.: Time series forecasting for the number of firefighters interventions. In: International Conference on Advanced Information Networking and Applications, pp. 39–50 (2021)
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