ARIMA models for the analysis of earthquake data in Peru in 2017

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This project seeks to explore the phenomenon of earthquakes through the use of statistical tools, in this case to characterize and search for patterns of behavior of earthquakes, which have occurred in Peru in 2017; for this purpose, techniques such as Cluster Analysis and Time Series Models are use...

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Detalles Bibliográficos
Autor: Risco Franco, Carlos
Formato: artículo
Fecha de Publicación:2017
Institución:Universidad Nacional de Ingeniería
Repositorio:Revistas - Universidad Nacional de Ingeniería
Lenguaje:español
inglés
OAI Identifier:oai:oai:revistas.uni.edu.pe:article/1179
Enlace del recurso:https://revistas.uni.edu.pe/index.php/iecos/article/view/1179
Nivel de acceso:acceso abierto
Materia:Modelos de Series de tiempo ARIMA
Cluster Analysis
sismo
Time Series Models
ARIMA Cluster Analysis
Earthquake
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spelling ARIMA models for the analysis of earthquake data in Peru in 2017Modelos ARIMA para el análisis de datos de sismos en el Perú en 2017Risco Franco, CarlosModelos de Series de tiempo ARIMACluster AnalysissismoTime Series ModelsARIMA Cluster AnalysisEarthquakeThis project seeks to explore the phenomenon of earthquakes through the use of statistical tools, in this case to characterize and search for patterns of behavior of earthquakes, which have occurred in Peru in 2017; for this purpose, techniques such as Cluster Analysis and Time Series Models are used. First, the zones of greatest seismic activity have been identified and then their main characteristics have been found, as well as the interrelation between magnitude and depth. Apart from identifying the seismic zones, a small decreasing trend in the magnitude of earthquakes in Lima in the last three months has been observed. The data have been fitted to an ARIMA (1,1,0) time series model, which has been found to be significant, both at the national level and for Lima-Ica and Arequipa. Data from the Peruvian Geophysical Institute on its web page has been used. The most active area is the Arequipa area, followed by Lima.Este proyecto busca explorar el fenómeno de los sismos mediante el uso de herramientas estadísticas, en este caso para caracterizar y buscar patrones de comportamiento de los mismos, que se han presentado en el Perú en el 2017; para ello se usan técnicas como el Cluster Análysis y Modelos de Series de Tiempo. Primero se ha identificado las zonas de mayor actividad sísmica y luego se ha hallado sus características principales, así como la interrelación entre magnitud y profundidad. Aparte de identificar las zonas sísmicas, se observa una pequeña tendencia a la disminución en la magnitud de los sismos en Lima en los últimos tres meses. Los datos se han ajustado a un modelo de series de tiempo ARIMA (1,1,0), el cual ha resultado significativo, tanto a nivel nacional, como para Lima-Ica y Arequipa. Se ha utilizado datos del Instituto Geofísico del Perú en su página web. La zona de mayor actividad es la zona de Arequipa, seguida de la de Lima.Universidad Nacional de Ingeniería2017-07-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionPeer ReviewedEvaluado por paresapplication/pdfaudio/mpegaudio/mpeghttps://revistas.uni.edu.pe/index.php/iecos/article/view/117910.21754/iecos.v18i0.1179revista IECOS; Vol. 18 (2017); 143-157Revista IECOS; Vol. 18 (2017); 143-1572788-74802961-284510.21754/iecos.v18i0reponame:Revistas - Universidad Nacional de Ingenieríainstname:Universidad Nacional de Ingenieríainstacron:UNIspaenghttps://revistas.uni.edu.pe/index.php/iecos/article/view/1179/3151https://revistas.uni.edu.pe/index.php/iecos/article/view/1179/3152https://revistas.uni.edu.pe/index.php/iecos/article/view/1179/3153Derechos de autor 2017 Carlos Risco Francohttps://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessoai:oai:revistas.uni.edu.pe:article/11792025-01-20T02:54:13Z
dc.title.none.fl_str_mv ARIMA models for the analysis of earthquake data in Peru in 2017
Modelos ARIMA para el análisis de datos de sismos en el Perú en 2017
title ARIMA models for the analysis of earthquake data in Peru in 2017
spellingShingle ARIMA models for the analysis of earthquake data in Peru in 2017
Risco Franco, Carlos
Modelos de Series de tiempo ARIMA
Cluster Analysis
sismo
Time Series Models
ARIMA Cluster Analysis
Earthquake
title_short ARIMA models for the analysis of earthquake data in Peru in 2017
title_full ARIMA models for the analysis of earthquake data in Peru in 2017
title_fullStr ARIMA models for the analysis of earthquake data in Peru in 2017
title_full_unstemmed ARIMA models for the analysis of earthquake data in Peru in 2017
title_sort ARIMA models for the analysis of earthquake data in Peru in 2017
dc.creator.none.fl_str_mv Risco Franco, Carlos
author Risco Franco, Carlos
author_facet Risco Franco, Carlos
author_role author
dc.subject.none.fl_str_mv Modelos de Series de tiempo ARIMA
Cluster Analysis
sismo
Time Series Models
ARIMA Cluster Analysis
Earthquake
topic Modelos de Series de tiempo ARIMA
Cluster Analysis
sismo
Time Series Models
ARIMA Cluster Analysis
Earthquake
description This project seeks to explore the phenomenon of earthquakes through the use of statistical tools, in this case to characterize and search for patterns of behavior of earthquakes, which have occurred in Peru in 2017; for this purpose, techniques such as Cluster Analysis and Time Series Models are used. First, the zones of greatest seismic activity have been identified and then their main characteristics have been found, as well as the interrelation between magnitude and depth. Apart from identifying the seismic zones, a small decreasing trend in the magnitude of earthquakes in Lima in the last three months has been observed. The data have been fitted to an ARIMA (1,1,0) time series model, which has been found to be significant, both at the national level and for Lima-Ica and Arequipa. Data from the Peruvian Geophysical Institute on its web page has been used. The most active area is the Arequipa area, followed by Lima.
publishDate 2017
dc.date.none.fl_str_mv 2017-07-01
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer Reviewed
Evaluado por pares
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://revistas.uni.edu.pe/index.php/iecos/article/view/1179
10.21754/iecos.v18i0.1179
url https://revistas.uni.edu.pe/index.php/iecos/article/view/1179
identifier_str_mv 10.21754/iecos.v18i0.1179
dc.language.none.fl_str_mv spa
eng
language spa
eng
dc.relation.none.fl_str_mv https://revistas.uni.edu.pe/index.php/iecos/article/view/1179/3151
https://revistas.uni.edu.pe/index.php/iecos/article/view/1179/3152
https://revistas.uni.edu.pe/index.php/iecos/article/view/1179/3153
dc.rights.none.fl_str_mv Derechos de autor 2017 Carlos Risco Franco
https://creativecommons.org/licenses/by/4.0
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Derechos de autor 2017 Carlos Risco Franco
https://creativecommons.org/licenses/by/4.0
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
audio/mpeg
audio/mpeg
dc.publisher.none.fl_str_mv Universidad Nacional de Ingeniería
publisher.none.fl_str_mv Universidad Nacional de Ingeniería
dc.source.none.fl_str_mv revista IECOS; Vol. 18 (2017); 143-157
Revista IECOS; Vol. 18 (2017); 143-157
2788-7480
2961-2845
10.21754/iecos.v18i0
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instname:Universidad Nacional de Ingeniería
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instacron_str UNI
institution UNI
reponame_str Revistas - Universidad Nacional de Ingeniería
collection Revistas - Universidad Nacional de Ingeniería
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