Abstract
We present two techniques for modeling time series of emergency events using data from 911 emergency calls in the city of Cuenca-Ecuador. We study state-of-the-art methods for time series analysis and assess the benefits and drawbacks of each one of them. In this paper, we develop an emergency model using a large dataset corresponding to the period January 1st 2015 through December 31st 2016 and test a Gaussian Process and an ARIMA model for temporal prediction purposes. We assess the performance of our approaches experimentally, comparing the standard residual error (SRE) and the execution time of both models. In addition, we include climate and holidays data as explanatory variables of the regressions aiming to improve the prediction. The results show that ARIMA model is the most suitable one for forecasting emergency events even without the support of additional variables.
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Acknowledgements
This article is part of the project “Análisis predictivo de la ocurrencia de eventos de emergencia en la provincia del Azuay”, winner of the “XV Concurso Universitario de Proyectos de Investigación” funded by the Dirección de Investigación de la Universidad de Cuenca. The authors also thank the Servicio Integrado de Seguridad ECU911 - Zona 6 for their collaboration and data provided.
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Robles, P., Tello, A., Solano-Quinde, L., Zúñiga-Prieto, M. (2020). Temporal Analysis of 911 Emergency Calls Through Time Series Modeling. In: Botto-Tobar, M., León-Acurio, J., Díaz Cadena, A., Montiel Díaz, P. (eds) Advances in Emerging Trends and Technologies. ICAETT 2019. Advances in Intelligent Systems and Computing, vol 1066. Springer, Cham. https://doi.org/10.1007/978-3-030-32022-5_13
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