Abstract
Time series forecasting is one of the most attractive analysis of dataset that involves a time component to extract meaningful results in economy, biology, meteorology, civil protection services, retail, etc. This paper aims to study three different time series forecasting algorithms and compare them to other models applied in previous researchers’ work as well as an application of Prophet tool launched by Facebook. This work relies on an hourly real dataset of firefighters’ interventions registered from 2006 till 2017 in the region of Doubs-France by the fire and rescue department. Each algorithm is explained with best fit parameters, statistical features are calculated and then compared between applied models on the same dataset.
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Acknowledgement
This work has been supported by the EIPHI Graduate School (contract ANR-17-EURE-0002) and is partially funded with support from the Hubert Curien CEDRE programme n 46543ZD.
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Elias Mallouhy, R., Guyeux, C., Abou Jaoude, C., Makhoul, A. (2021). Time Series Forecasting for the Number of Firefighters Interventions. In: Barolli, L., Woungang, I., Enokido, T. (eds) Advanced Information Networking and Applications. AINA 2021. Lecture Notes in Networks and Systems, vol 225. Springer, Cham. https://doi.org/10.1007/978-3-030-75100-5_4
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