@inproceedings{0555954b8f554a9bac5304b15d5994f6,
title = "Temporal Analysis of 911 Emergency Calls Through Time Series Modeling",
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.",
keywords = "911 calls, ARIMA, Emergency calls, GP, Temporal models",
author = "Pablo Robles and Andr{\'e}s Tello and Lizandro Solano-Quinde and Miguel Z{\'u}{\~n}iga-Prieto",
note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; 1st International Conference on Advances in Emerging Trends and Technologies, ICAETT 2019 ; Conference date: 29-05-2019 Through 31-05-2019",
year = "2020",
doi = "10.1007/978-3-030-32022-5\_13",
language = "Ingl{\'e}s",
isbn = "9783030320218",
series = "Advances in Intelligent Systems and Computing",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "136--145",
editor = "Miguel Botto-Tobar and Joffre Le{\'o}n-Acurio and \{D{\'i}az Cadena\}, Angela and \{Montiel D{\'i}az\}, Pr{\'a}xedes",
booktitle = "Advances in Emerging Trends and Technologies Volume 1",
}