Estimation of missing data in a geophysical series of precipitation
The analysis of dynamic systems is a topic of great interest in the basic sciences since it allows direct inference of the behavior of different systems. The study of physical phenomena provides large databases that, if recorded at regular time intervals, constitute time series. However, time series...
- Autores:
-
Vergel Ortega, Mawency
GALLARDO PÉREZ, HENRY DE JESÚS
Rojas Suárez, Jhan Piero
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2021
- Institución:
- Universidad Francisco de Paula Santander
- Repositorio:
- Repositorio Digital UFPS
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.ufps.edu.co:ufps/721
- Acceso en línea:
- http://repositorio.ufps.edu.co/handle/ufps/721
- Palabra clave:
- Rights
- openAccess
- License
- Atribución 4.0 Internacional (CC BY 4.0)
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Estimation of missing data in a geophysical series of precipitation |
| title |
Estimation of missing data in a geophysical series of precipitation |
| spellingShingle |
Estimation of missing data in a geophysical series of precipitation |
| title_short |
Estimation of missing data in a geophysical series of precipitation |
| title_full |
Estimation of missing data in a geophysical series of precipitation |
| title_fullStr |
Estimation of missing data in a geophysical series of precipitation |
| title_full_unstemmed |
Estimation of missing data in a geophysical series of precipitation |
| title_sort |
Estimation of missing data in a geophysical series of precipitation |
| dc.creator.fl_str_mv |
Vergel Ortega, Mawency GALLARDO PÉREZ, HENRY DE JESÚS Rojas Suárez, Jhan Piero |
| dc.contributor.author.none.fl_str_mv |
Vergel Ortega, Mawency GALLARDO PÉREZ, HENRY DE JESÚS Rojas Suárez, Jhan Piero |
| description |
The analysis of dynamic systems is a topic of great interest in the basic sciences since it allows direct inference of the behavior of different systems. The study of physical phenomena provides large databases that, if recorded at regular time intervals, constitute time series. However, time series of geophysical data in many cases present missing data and their estimation requires the application of valid methods that allow estimating reliable information to complete the series since some analysis methods require these series to be complete. Two methods are used in this article to estimate the missing values of the precipitation series in the city of San José de Cúcuta, Colombia, the first one consists of considering the univariate data series and applying an adjustment of the sequential conditional expectation method of forecasting with restrictions, the second one refers to analyze the data series of a nearby station and through multivariate methods establish the cointegration between the series, and then use this as a basis for estimating the missing data in the analysis series. The two methods are recursive, a first estimation of the model is made ignoring the missing data, an initial estimation of the missing data is made, then a new estimation of the model parameters and a new estimation of the missing data is made, the algorithm continues running with the new values replacing the values estimated in the previous phase until the difference of the estimated values between successive iterations is less than a value fixed beforehand. Finally, a comparison is made between the estimates made by the two methods. |
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2021 |
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2021-11-07T14:15:41Z |
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2021-11-07T14:15:41Z |
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2021-06-09 |
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Artículo de revista |
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10.1088/1742-6596/1938/1/012024 |
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eng |
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eng |
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Journal of Physics: Conference Series |
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6 |
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1 (2021) |
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1938 |
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Pérez, H. G., Ortega, M. V., & Suárez, J. R. (2021, May). Estimation of missing data in a geophysical series of precipitation. In Journal of Physics: Conference Series (Vol. 1938, No. 1, p. 012024). IOP Publishing. |
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Atribución 4.0 Internacional (CC BY 4.0) |
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Vergel Ortega, Mawencye1db451514df4d6eb054b4e8e3bf1e42600GALLARDO PÉREZ, HENRY DE JESÚS65e8d56df3770f30fa30193266191212600Rojas Suárez, Jhan Piero96cb752d974d2a7f4f66513af6ebbf8d6002021-11-07T14:15:41Z2021-11-07T14:15:41Z2021-06-09http://repositorio.ufps.edu.co/handle/ufps/72110.1088/1742-6596/1938/1/012024The analysis of dynamic systems is a topic of great interest in the basic sciences since it allows direct inference of the behavior of different systems. The study of physical phenomena provides large databases that, if recorded at regular time intervals, constitute time series. However, time series of geophysical data in many cases present missing data and their estimation requires the application of valid methods that allow estimating reliable information to complete the series since some analysis methods require these series to be complete. Two methods are used in this article to estimate the missing values of the precipitation series in the city of San José de Cúcuta, Colombia, the first one consists of considering the univariate data series and applying an adjustment of the sequential conditional expectation method of forecasting with restrictions, the second one refers to analyze the data series of a nearby station and through multivariate methods establish the cointegration between the series, and then use this as a basis for estimating the missing data in the analysis series. The two methods are recursive, a first estimation of the model is made ignoring the missing data, an initial estimation of the missing data is made, then a new estimation of the model parameters and a new estimation of the missing data is made, the algorithm continues running with the new values replacing the values estimated in the previous phase until the difference of the estimated values between successive iterations is less than a value fixed beforehand. Finally, a comparison is made between the estimates made by the two methods.application/pdfengJournal of Physics: Conference SeriesJournal of Physics: Conference SeriesVol.1938 No.1.(2021)61 (2021)11938Pérez, H. G., Ortega, M. V., & Suárez, J. R. (2021, May). Estimation of missing data in a geophysical series of precipitation. In Journal of Physics: Conference Series (Vol. 1938, No. 1, p. 012024). IOP Publishing.Journal of Physics: Conference SeriesContent from this work may be used under the terms of theCreative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. Published under licence by IOP Publishing Ltdinfo:eu-repo/semantics/openAccessAtribución 4.0 Internacional (CC BY 4.0)http://purl.org/coar/access_right/c_abf2https://iopscience.iop.org/article/10.1088/1742-6596/1938/1/012024/metaEstimation of missing data in a geophysical series of precipitationArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85Herrera-Oliva C, Campos-Gaytán J and Carrillo-González F 2017 Estimación de datos faltantes de precipitación por el método de regresión lineal: Caso de estudio Cuenca Guadalupe, Baja California, México Investigación y Ciencia 25 33Gallardo H, Vergel M and Rojas J 2020 Análisis dinámico de series multivariadas Mundo FESC 10 34Gallardo H, Rojas J and Gallardo O 2019 Modelación de Series Temporales en el Sector Productivo del Norte de Santander (Bogotá: ECOE)Gallardo H, Vergel M and Rojas J 2020 Dynamic and sequential update for time series forecasting Journal of Physics: Conference Series 1587 1Mauricio J 2007 Introducción al Análisis de Series Temporales (Madrid: Universidad Complutense de Madrid)Abril J 2011 Análisis de la evolución de las técnicas de series tiempo. Un enfoque unificado Estadística 63 5Box G and Jenkins G 1969 Time Series Analysis, Forecasting and Control (San Francisco: Holden–Day)Gallardo H, Gallardo O and Rojas J 2019 Estimation of models and cycles in time series applying fractal geometry Journal of Physics: Conference Series 1329 1Guerrero V 1989 Optimal conditional ARIMA forecasts Journal of Forecasting 8 215Medina-Rivera R, Montoya-Restrepo E and Jaramillo-Robledo A 2008 Estimación estadística de valores faltantes en series históricas de lluvia Cenicafé 59 260Box G and Tiao G 1975 Intervention analysis with applications to economic and environmental problems Journal of the American Statistical Association 70 335Chow G and Lin A 1976 Best linear unbiased estimation of missing observation in a economic time series Journal of the American Statistical Association 71 719Anderson B and Moore B 1979 Optimal Filtering (Englewood: Prentice-Hall)Jones R 1980 Maximum likelihood fitting of ARMA Models to time series with missing observations Technometrics 22 389Peña D and Maravall A 1990 Interpolation, outliers and the inverse autocorrelations Communications in Statistics 20 3175Velásquez M and Martínez J 2009 Estimación de observaciones faltantes en series de tiempo usando métodos multivariados con restricciones Comunicaciones en Estadística 2 1Guerrero V and Peña D 2003 Combining multiple time series predictors: a useful inferential procedure Journal of Statistical Planning and Inference 116 249Alfaro E and Javier F 2009 Descripción de dos métodos de rellenado de datos ausentes en series de tiempo meteorológicas Revista de Matemáticas: Teoría y Aplicaciones 16 5WeatherOnline Ltd 2020 WeatherOnline Ltd. - Meteorological Services (London: WeatherOnline Ltd) Consulted on: www.woespana.esTEXTEstimation of missing data in a geophysical series of precipitation.pdf.txtEstimation of missing data in a geophysical series of precipitation.pdf.txtExtracted 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charset=utf-814828https://repositorio.ufps.edu.co/bitstream/ufps/721/2/license.txt2f9959eaf5b71fae44bbf9ec84150c7aMD52open accessufps/721oai:repositorio.ufps.edu.co:ufps/7212022-05-23 10:49:34.583open accessRepositorio Universidad Francisco de Paula 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 incorporada en las Obras Colectivas.

b.	Distribuir copias o fonogramas de las Obras, exhibirlas públicamente, ejecutarlas públicamente y/o ponerlas a disposición pública, incluyéndolas como incorporadas en Obras Colectivas, según corresponda.

c.	Distribuir copias de las Obras Derivadas que se generen, exhibirlas públicamente, ejecutarlas públicamente y/o ponerlas a disposición pública.
Los derechos mencionados anteriormente pueden ser ejercidos en todos los medios y formatos, actualmente conocidos o que se inventen en el futuro. Los derechos antes mencionados incluyen el derecho a realizar dichas modificaciones en la medida que sean técnicamente necesarias para ejercer los derechos en otro medio o formatos, pero de otra manera usted no está autorizado para realizar obras derivadas. Todos los derechos no otorgados expresamente por el Licenciante quedan por este medio reservados, incluyendo pero sin limitarse a aquellos que se mencionan en las secciones 4(d) y 4(e).

4. Restricciones.
La licencia otorgada en la anterior Sección 3 está expresamente sujeta y limitada por las siguientes restricciones:

a.	Usted puede distribuir, exhibir públicamente, ejecutar públicamente, o poner a disposición pública la Obra sólo bajo las condiciones de esta Licencia, y Usted debe incluir una copia de esta licencia o del Identificador Universal de Recursos de la misma con cada copia de la Obra que distribuya, exhiba públicamente, ejecute públicamente o ponga a disposición pública. No es posible ofrecer o imponer ninguna condición sobre la Obra que altere o limite las condiciones de esta Licencia o el ejercicio de los derechos de los destinatarios otorgados en este documento. No es posible sublicenciar la Obra. Usted debe mantener intactos todos los avisos que hagan referencia a esta Licencia y a la cláusula de limitación de garantías. Usted no puede distribuir, exhibir públicamente, ejecutar públicamente, o poner a disposición pública la Obra con alguna medida tecnológica que controle el acceso o la utilización de ella de una forma que sea inconsistente con las condiciones de esta Licencia. Lo anterior se aplica a la Obra incorporada a una Obra Colectiva, pero esto no exige que la Obra Colectiva aparte de la obra misma quede sujeta a las condiciones de esta Licencia. Si Usted crea una Obra Colectiva, previo aviso de cualquier Licenciante debe, en la medida de lo posible, eliminar de la Obra Colectiva cualquier referencia a dicho Licenciante o al Autor Original, según lo solicitado por el Licenciante y conforme lo exige la cláusula 4(c).

b.	Usted no puede ejercer ninguno de los derechos que le han sido otorgados en la Sección 3 precedente de modo que estén principalmente destinados o directamente dirigidos a conseguir un provecho comercial o una compensación monetaria privada. El intercambio de la Obra por otras obras protegidas por derechos de autor, ya sea a través de un sistema para compartir archivos digitales (digital file-sharing) o de cualquier otra manera no será considerado como estar destinado principalmente o dirigido directamente a conseguir un provecho comercial o una compensación monetaria privada, siempre que no se realice un pago mediante una compensación monetaria en relación con el intercambio de obras protegidas por el derecho de autor.

c.	Si usted distribuye, exhibe públicamente, ejecuta públicamente o ejecuta públicamente en forma digital la Obra o cualquier Obra Derivada u Obra Colectiva, Usted debe mantener intacta toda la información de derecho de autor de la Obra y proporcionar, de forma razonable según el medio o manera que Usted esté utilizando: (i) el nombre del Autor Original si está provisto (o seudónimo, si fuere aplicable), y/o (ii) el nombre de la parte o las partes que el Autor Original y/o el Licenciante hubieren designado para la atribución (v.g., un instituto patrocinador, editorial, publicación) en la información de los derechos de autor del Licenciante, términos de servicios o de otras formas razonables; el título de la Obra si está provisto; en la medida de lo razonablemente factible y, si está provisto, el Identificador Uniforme de Recursos (Uniform Resource Identifier) que el Licenciante especifica para ser asociado con la Obra, salvo que tal URI no se refiera a la nota sobre los derechos de autor o a la información sobre el licenciamiento de la Obra; y en el caso de una Obra Derivada, atribuir el crédito identificando el uso de la Obra en la Obra Derivada (v.g., "Traducción Francesa de la Obra del Autor Original," o "Guión Cinematográfico basado en la Obra original del Autor Original"). Tal crédito puede ser implementado de cualquier forma razonable; en el caso, sin embargo, de Obras Derivadas u Obras Colectivas, tal crédito aparecerá, como mínimo, donde aparece el crédito de cualquier otro autor comparable y de una manera, al menos, tan destacada como el crédito de otro autor comparable.

d.	Para evitar toda confusión, el Licenciante aclara que, cuando la obra es una composición musical:

i.	Regalías por interpretación y ejecución bajo licencias generales. El Licenciante se reserva el derecho exclusivo de autorizar la ejecución pública o la ejecución pública digital de la obra y de recolectar, sea individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, SAYCO), las regalías por la ejecución pública o por la ejecución pública digital de la obra (por ejemplo Webcast) licenciada bajo licencias generales, si la interpretación o ejecución de la obra está primordialmente orientada por o dirigida a la obtención de una ventaja comercial o una compensación monetaria privada.

ii.	Regalías por Fonogramas. El Licenciante se reserva el derecho exclusivo de recolectar, individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, los consagrados por la SAYCO), una agencia de derechos musicales o algún agente designado, las regalías por cualquier fonograma que Usted cree a partir de la obra (“versión cover”) y distribuya, en los términos del régimen de derechos de autor, si la creación o distribución de esa versión cover está primordialmente destinada o dirigida a obtener una ventaja comercial o una compensación monetaria privada.

e.	Gestión de Derechos de Autor sobre Interpretaciones y Ejecuciones Digitales (WebCasting). Para evitar toda confusión, el Licenciante aclara que, cuando la obra sea un fonograma, el Licenciante se reserva el derecho exclusivo de autorizar la ejecución pública digital de la obra (por ejemplo, webcast) y de recolectar, individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, ACINPRO), las regalías por la ejecución pública digital de la obra (por ejemplo, webcast), sujeta a las disposiciones aplicables del régimen de Derecho de Autor, si esta ejecución pública digital está primordialmente dirigida a obtener una ventaja comercial o una compensación monetaria privada.

5. Representaciones, Garantías y Limitaciones de Responsabilidad.
A MENOS QUE LAS PARTES LO ACORDARAN DE OTRA FORMA POR ESCRITO, EL LICENCIANTE OFRECE LA OBRA (EN EL ESTADO EN EL QUE SE ENCUENTRA) “TAL CUAL”, SIN BRINDAR GARANTÍAS DE CLASE ALGUNA RESPECTO DE LA OBRA, YA SEA EXPRESA, IMPLÍCITA, LEGAL O CUALQUIERA OTRA, INCLUYENDO, SIN LIMITARSE A ELLAS, GARANTÍAS DE TITULARIDAD, COMERCIABILIDAD, ADAPTABILIDAD O ADECUACIÓN A PROPÓSITO DETERMINADO, AUSENCIA DE INFRACCIÓN, DE AUSENCIA DE DEFECTOS LATENTES O DE OTRO TIPO, O LA PRESENCIA O AUSENCIA DE ERRORES, SEAN O NO DESCUBRIBLES (PUEDAN O NO SER ESTOS DESCUBIERTOS). ALGUNAS JURISDICCIONES NO PERMITEN LA EXCLUSIÓN DE GARANTÍAS IMPLÍCITAS, EN CUYO CASO ESTA EXCLUSIÓN PUEDE NO APLICARSE A USTED.

6. Limitación de responsabilidad.
A MENOS QUE LO EXIJA EXPRESAMENTE LA LEY APLICABLE, EL LICENCIANTE NO SERÁ RESPONSABLE ANTE USTED POR DAÑO ALGUNO, SEA POR RESPONSABILIDAD EXTRACONTRACTUAL, PRECONTRACTUAL O CONTRACTUAL, OBJETIVA O SUBJETIVA, SE TRATE DE DAÑOS MORALES O PATRIMONIALES, DIRECTOS O INDIRECTOS, PREVISTOS O IMPREVISTOS PRODUCIDOS POR EL USO DE ESTA LICENCIA O DE LA OBRA, AUN CUANDO EL LICENCIANTE HAYA SIDO ADVERTIDO DE LA POSIBILIDAD DE DICHOS DAÑOS. ALGUNAS LEYES NO PERMITEN LA EXCLUSIÓN DE CIERTA RESPONSABILIDAD, EN CUYO CASO ESTA EXCLUSIÓN PUEDE NO APLICARSE A USTED.

7. Término.

a.	Esta Licencia y los derechos otorgados en virtud de ella terminarán automáticamente si Usted infringe alguna condición establecida en ella. Sin embargo, los individuos o entidades que han recibido Obras Derivadas o Colectivas de Usted de conformidad con esta Licencia, no verán terminadas sus licencias, siempre que estos individuos o entidades sigan cumpliendo íntegramente las condiciones de estas licencias. Las Secciones 1, 2, 5, 6, 7, y 8 subsistirán a cualquier terminación de esta Licencia.

b.	Sujeta a las condiciones y términos anteriores, la licencia otorgada aquí es perpetua (durante el período de vigencia de los derechos de autor de la obra). No obstante lo anterior, el Licenciante se reserva el derecho a publicar y/o estrenar la Obra bajo condiciones de licencia diferentes o a dejar de distribuirla en los términos de esta Licencia en cualquier momento; en el entendido, sin embargo, que esa elección no servirá para revocar esta licencia o que deba ser otorgada , bajo los términos de esta licencia), y esta licencia continuará en pleno vigor y efecto a menos que sea terminada como se expresa atrás. La Licencia revocada continuará siendo plenamente vigente y efectiva si no se le da término en las condiciones indicadas anteriormente.

8. Varios.

a.	Cada vez que Usted distribuya o ponga a disposición pública la Obra o una Obra Colectiva, el Licenciante ofrecerá al destinatario una licencia en los mismos términos y condiciones que la licencia otorgada a Usted bajo esta Licencia.

b.	Si alguna disposición de esta Licencia resulta invalidada o no exigible, según la legislación vigente, esto no afectará ni la validez ni la aplicabilidad del resto de condiciones de esta Licencia y, sin acción adicional por parte de los sujetos de este acuerdo, aquélla se entenderá reformada lo mínimo necesario para hacer que dicha disposición sea válida y exigible.

c.	Ningún término o disposición de esta Licencia se estimará renunciada y ninguna violación de ella será consentida a menos que esa renuncia o consentimiento sea otorgado por escrito y firmado por la parte que renuncie o consienta.

d.	Esta Licencia refleja el acuerdo pleno entre las partes respecto a la Obra aquí licenciada. No hay arreglos, acuerdos o declaraciones respecto a la Obra que no estén especificados en este documento. El Licenciante no se verá limitado por ninguna disposición adicional que pueda surgir en alguna comunicación emanada de Usted. Esta Licencia no puede ser modificada sin el consentimiento mutuo por escrito del Licenciante y Usted.
0000-0001-8285-2968e1db451514df4d6eb054b4e8e3bf1e426000000-0003-4377-390365e8d56df3770f30fa301932661912126000000-0003-2682-988096cb752d974d2a7f4f66513af6ebbf8d600 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