TY - GEN
T1 - Temporal Analysis of 911 Emergency Calls Through Time Series Modeling
AU - Robles, Pablo
AU - Tello, Andrés
AU - Solano-Quinde, Lizandro
AU - Zúñiga-Prieto, Miguel
N1 - Funding Information:
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 Inte-grado de Seguridad ECU911 - Zona 6 for their collaboration and data provided.
Funding Information:
This article is part of the project ?An?lisis predictivo de la ocurrencia de eventos de emergencia en la provincia del Azuay?, winner of the ?XV Concurso Universitario de Proyectos de Investigaci?n? funded by the Direcci?n de Investigaci?n de la Universidad de Cuenca. The authors also thank the Servicio Integrado de Seguridad ECU911-Zona 6 for their collaboration and data provided.
Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
Copyright:
Copyright 2019 Elsevier B.V., All rights reserved.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - 911 calls
KW - ARIMA
KW - Emergency calls
KW - GP
KW - Temporal models
UR - http://www.scopus.com/inward/record.url?scp=85075643669&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-32022-5_13
DO - 10.1007/978-3-030-32022-5_13
M3 - Conference contribution
AN - SCOPUS:85075643669
SN - 9783030320218
T3 - Advances in Intelligent Systems and Computing
SP - 136
EP - 145
BT - Advances in Emerging Trends and Technologies
A2 - Botto-Tobar, Miguel
A2 - León-Acurio, Joffre
A2 - Díaz Cadena, Angela
A2 - Montiel Díaz, Práxedes
PB - Springer
T2 - 1st International Conference on Advances in Emerging Trends and Technologies, ICAETT 2019
Y2 - 29 May 2019 through 31 May 2019
ER -