Dynamic and sequential update for time series forecasting
Two different sciences, physics and statistics, have worked, from the foundations of each, on the explanation and modelling of stochastic processes characterized by the succession of random variables whose realizations at each instant of time give rise to time series. From Physics we have worked wit...
- Autores:
-
GALLARDO PÉREZ, HENRY DE JESÚS
Vergel Ortega, Mawency
Rojas Suárez, Jhan Piero
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2020
- Institución:
- UNIVERSIDAD FRANCISCO DE PAULA SANTANDER
- Repositorio:
- Repositorio Digital UFPS
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.ufps.edu.co:ufps/810
- Acceso en línea:
- http://repositorio.ufps.edu.co/handle/ufps/810
- Palabra clave:
- Rights
- openAccess
- License
- Atribución 4.0 Internacional (CC BY 4.0)
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Dynamic and sequential update for time series forecasting |
| title |
Dynamic and sequential update for time series forecasting |
| spellingShingle |
Dynamic and sequential update for time series forecasting |
| title_short |
Dynamic and sequential update for time series forecasting |
| title_full |
Dynamic and sequential update for time series forecasting |
| title_fullStr |
Dynamic and sequential update for time series forecasting |
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Dynamic and sequential update for time series forecasting |
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Dynamic and sequential update for time series forecasting |
| dc.creator.fl_str_mv |
GALLARDO PÉREZ, HENRY DE JESÚS Vergel Ortega, Mawency Rojas Suárez, Jhan Piero |
| dc.contributor.author.none.fl_str_mv |
GALLARDO PÉREZ, HENRY DE JESÚS Vergel Ortega, Mawency Rojas Suárez, Jhan Piero |
| description |
Two different sciences, physics and statistics, have worked, from the foundations of each, on the explanation and modelling of stochastic processes characterized by the succession of random variables whose realizations at each instant of time give rise to time series. From Physics we have worked with the Fourier transform to explain the dynamics of time series, a similar case occurs from statistics where dynamic models of time series are worked to explain the variations of the series and, in both cases, to make reliable forecasts. The main objective of this research is to adjust a model, using the methodology framed in the sequential update procedure of the forecast, to a time series of coal production observed quarterly during the years 2007 to 2011, in order to disaggregate quarterly the annual production for the years 2012 to 2018. Once the process has been carried out and validated, a quarterly production model is estimated which allows valid and reliable forecasts to be made for each quarter in subsequent years. |
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2020 |
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2020-08-05 |
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2021-11-09T20:34:56Z |
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2021-11-09T20:34:56Z |
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Artículo de revista |
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10.1088/1742-6596/1587/1/012016 |
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eng |
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eng |
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Journal of Physics: Conference Series |
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Vol.1587 No.1.(2020) |
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12016-7 |
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1 (2020) |
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12016-1 |
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1587 |
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Pérez, H. G., Ortega, M. V., & Rojas-Suárez, J. P. (2020, July). Dynamic and sequential update for time series forecasting. In Journal of Physics: Conference Series (Vol. 1587, No. 1, p. 012016). IOP Publishing. |
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Atribución 4.0 Internacional (CC BY 4.0) |
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openAccess |
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Atribución 4.0 Internacional (CC BY 4.0) http://purl.org/coar/access_right/c_abf2 |
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Journal of Physics: Conference Series |
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GALLARDO PÉREZ, HENRY DE JESÚS65e8d56df3770f30fa30193266191212600Vergel Ortega, Mawencye1db451514df4d6eb054b4e8e3bf1e42600Rojas Suárez, Jhan Piero96cb752d974d2a7f4f66513af6ebbf8d6002021-11-09T20:34:56Z2021-11-09T20:34:56Z2020-08-05http://repositorio.ufps.edu.co/handle/ufps/81010.1088/1742-6596/1587/1/012016Two different sciences, physics and statistics, have worked, from the foundations of each, on the explanation and modelling of stochastic processes characterized by the succession of random variables whose realizations at each instant of time give rise to time series. From Physics we have worked with the Fourier transform to explain the dynamics of time series, a similar case occurs from statistics where dynamic models of time series are worked to explain the variations of the series and, in both cases, to make reliable forecasts. The main objective of this research is to adjust a model, using the methodology framed in the sequential update procedure of the forecast, to a time series of coal production observed quarterly during the years 2007 to 2011, in order to disaggregate quarterly the annual production for the years 2012 to 2018. Once the process has been carried out and validated, a quarterly production model is estimated which allows valid and reliable forecasts to be made for each quarter in subsequent years.application/pdfengJournal of Physics: Conference SeriesJournal of Physics: Conference SeriesVol.1587 No.1.(2020)12016-71 (2020)12016-11587Pérez, H. G., Ortega, M. V., & Rojas-Suárez, J. P. (2020, July). Dynamic and sequential update for time series forecasting. In Journal of Physics: Conference Series (Vol. 1587, No. 1, p. 012016). IOP Publishing.Journal of Physics: Conference SeriesContent from this work may be used under the terms of the Creative 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/10.1088/1742-6596/1587/1/012016/metaDynamic and sequential update for time series forecastingArtículo 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_970fb48d4fbd8a85Unidad de Planeación Minero-Energética 2019 Boletín Estadístico de Minas y Energía (Bogotá: Ministerio de Minas y Energía)Unidad de Planeación Minero-Energética 2019 Sistema de Información Minero Energético Colombiano (Bogotá: Ministerio de Minas y Energía)Peña D 1990 Estadística Modelos y Métodos: 2. Modelos Lineales y Series Temporales (Madrid: Alianza Editorial)Box 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 012018 1Saavedra V, Fernández T, Harmony T and Castro V 2006 Ondeletas en ingeniería, principios y aplicaciones Ingeniería, Investigación y Tecnología 7 185Guerrero V 1991 Análisis Estadístico de Series de Tiempo Económicas (México: Universidad Autónoma Metropolitana)Peña D 2010 Análisis de Series Temporales (Barcelona: Alianza Editorial)Gao J, Cao Y, Tung W and Hu J 2007 Multiscale Analysis of Complex Time Series: Integration of Chaos and Random Fractal Theory, and Beyond (New Jersey: John Wiley & Sons)Gallardo H, Rojas J and Gallardo O 2019 Modelación de Series Temporales en el Sector Productivo del Norte de Santander (Bogotá: ECOE)Madrigal S 2014 Modelos de regresión para el pronóstico de series temporales con estacionalidad creciente Computación y Sistemas 18 821Render B, Stair R and Hanna M 2006 Métodos Cuantitativos para los Negocios (México: Pearson)Brockwell P and Davis R 2002 Introduction to Time Series and Forecasting (New York: Springer)Frances P 1998 Time Series Models for Business and Economic Forecasting (Cambridge: University Press)Shumway R and Stoffer D 2017 Time Series Analysis and its Applications (Gewerbestrasse: Springer)White G 2010 Introducción al Análisis de Vibraciones (New York: Azima DLI)Nores M and Díaz M 2005 Construcción de modelos gee para variables con distribución simétrica Revista de la Sociedad Argentina de Estadística 9 43Medina R, Montoya E and Jaramillo A 2008 Estimación estadística de valores faltantes en series históricas de lluvia Cenicafé 59 260Mauricio J 2007 Introducción al Análisis de Series Multivariadas (Madrid: Universidad Complutense de Madrid)ORIGINALDynamic and sequential update for time series 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Thumbnailimage/jpeg8763https://repositorio.ufps.edu.co/bitstream/ufps/810/4/Dynamic%20and%20sequential%20update%20for%20time%20series%20forecasting.pdf.jpg9d94129233e97f09618e7626fb97c789MD54open accessufps/810oai:repositorio.ufps.edu.co:ufps/8102022-05-23 10:49:55.532open 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.
0000-0003-4377-390365e8d56df3770f30fa301932661912126000000-0001-8285-2968e1db451514df4d6eb054b4e8e3bf1e426000000-0003-2682-988096cb752d974d2a7f4f66513af6ebbf8d600 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