Temporal Analysis of 911 Emergency Calls Through Time Series Modeling

Pablo Robles; Andrés Tello; Lizandro Solano-Quinde; Miguel Zúñiga-Prieto

doi:10.1007/978-3-030-32022-5_13

Temporal Analysis of 911 Emergency Calls Through Time Series Modeling

Pablo Robles
, Andrés Tello
, Lizandro Solano-Quinde
, Miguel Zúñiga-Prieto

Universidad de Cuenca

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

Resumen

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.

Idioma original	Inglés
Título de la publicación alojada	Advances in Emerging Trends and Technologies Volume 1
Editores	Miguel Botto-Tobar, Joffre León-Acurio, Angela Díaz Cadena, Práxedes Montiel Díaz
Editorial	Springer Science and Business Media Deutschland GmbH
Páginas	136-145
Número de páginas	10
ISBN (versión impresa)	9783030320218
DOI	https://doi.org/10.1007/978-3-030-32022-5_13
Estado	Publicada - 2020
Evento	1st International Conference on Advances in Emerging Trends and Technologies, ICAETT 2019 - quito, Ecuador Duración: 29 may 2019 → 31 may 2019

Serie de la publicación

Nombre	Advances in Intelligent Systems and Computing
Volumen	1066
ISSN (versión impresa)	2194-5357
ISSN (versión digital)	2194-5365

Conferencia

Conferencia	1st International Conference on Advances in Emerging Trends and Technologies, ICAETT 2019
País/Territorio	Ecuador
Ciudad	quito
Período	29/05/19 → 31/05/19

Acceder al documento

10.1007/978-3-030-32022-5_13

Otros archivos y enlaces

Enlace a la publicación en Scopus

Análisis Predictivo De La Ocurrencia De Eventos De Emergencia En La Provincia Del Azuay
Solano Quinde, L. D. (Director), Zuñiga Prieto, M. A. (Co-Director), Tello Guerrero, M. A. (Investigador) & Robles Granda, P. D. (Personal Tecnico Asimilado)
1/06/17 → 31/05/19
Proyecto: Investigación

Citar esto

Robles, P., Tello, A., Solano-Quinde, L., & Zúñiga-Prieto, M. (2020). Temporal Analysis of 911 Emergency Calls Through Time Series Modeling. En M. Botto-Tobar, J. León-Acurio, A. Díaz Cadena, & P. Montiel Díaz (Eds.), Advances in Emerging Trends and Technologies Volume 1 (pp. 136-145). (Advances in Intelligent Systems and Computing; Vol. 1066). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-32022-5_13

Robles, Pablo ; Tello, Andrés ; Solano-Quinde, Lizandro et al. / Temporal Analysis of 911 Emergency Calls Through Time Series Modeling. Advances in Emerging Trends and Technologies Volume 1. editor / Miguel Botto-Tobar ; Joffre León-Acurio ; Angela Díaz Cadena ; Práxedes Montiel Díaz. Springer Science and Business Media Deutschland GmbH, 2020. pp. 136-145 (Advances in Intelligent Systems and Computing).

@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.",

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doi = "10.1007/978-3-030-32022-5\_13",

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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. En M Botto-Tobar, J León-Acurio, A Díaz Cadena & P Montiel Díaz (eds.), Advances in Emerging Trends and Technologies Volume 1. Advances in Intelligent Systems and Computing, vol. 1066, Springer Science and Business Media Deutschland GmbH, pp. 136-145, 1st International Conference on Advances in Emerging Trends and Technologies, ICAETT 2019, quito, Ecuador, 29/05/19. https://doi.org/10.1007/978-3-030-32022-5_13

Temporal Analysis of 911 Emergency Calls Through Time Series Modeling. / Robles, Pablo; Tello, Andrés; Solano-Quinde, Lizandro et al.
Advances in Emerging Trends and Technologies Volume 1. ed. / Miguel Botto-Tobar; Joffre León-Acurio; Angela Díaz Cadena; Práxedes Montiel Díaz. Springer Science and Business Media Deutschland GmbH, 2020. p. 136-145 (Advances in Intelligent Systems and Computing; Vol. 1066).

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

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

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

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DO - 10.1007/978-3-030-32022-5_13

M3 - Contribución a la conferencia

AN - SCOPUS:85075643669

SN - 9783030320218

T3 - Advances in Intelligent Systems and Computing

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BT - Advances in Emerging Trends and Technologies Volume 1

A2 - Botto-Tobar, Miguel

A2 - León-Acurio, Joffre

A2 - Díaz Cadena, Angela

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ER -

Robles P, Tello A, Solano-Quinde L, Zúñiga-Prieto M. Temporal Analysis of 911 Emergency Calls Through Time Series Modeling. En Botto-Tobar M, León-Acurio J, Díaz Cadena A, Montiel Díaz P, editores, Advances in Emerging Trends and Technologies Volume 1. Springer Science and Business Media Deutschland GmbH. 2020. p. 136-145. (Advances in Intelligent Systems and Computing). doi: 10.1007/978-3-030-32022-5_13

Temporal Analysis of 911 Emergency Calls Through Time Series Modeling

Resumen

Serie de la publicación

Conferencia

Acceder al documento

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Huella

Proyectos

Análisis Predictivo De La Ocurrencia De Eventos De Emergencia En La Provincia Del Azuay

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