Basics for Forecasting a Stationary Time Series Using Information from Its Past
Descripción del Articulo
Since market behavior is volatile, this research intends to help investors and business organizations make forecasts with certainty and, as a consequence, with the least possible error in order to succeed in the management of their projects and operations. Elements such as inflation rate, exchange r...
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Formato: | artículo |
Fecha de Publicación: | 2020 |
Institución: | Universidad Nacional Mayor de San Marcos |
Repositorio: | Revistas - Universidad Nacional Mayor de San Marcos |
Lenguaje: | español inglés |
OAI Identifier: | oai:ojs.csi.unmsm:article/16504 |
Enlace del recurso: | https://revistasinvestigacion.unmsm.edu.pe/index.php/idata/article/view/16504 |
Nivel de acceso: | acceso abierto |
Materia: | time series stationarity unit root white noise variance series de tiempo estacionariedad raíz unitaria ruido blanco varianza |
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Basics for Forecasting a Stationary Time Series Using Information from Its PastFundamentos para pronosticar una serie de tiempo estacionaria con información de su propio pasadoBazán Ramírez, Wilfredotime seriesstationarityunit rootwhite noisevarianceseries de tiempoestacionariedadraíz unitariaruido blancovarianzaSince market behavior is volatile, this research intends to help investors and business organizations make forecasts with certainty and, as a consequence, with the least possible error in order to succeed in the management of their projects and operations. Elements such as inflation rate, exchange rate, stock prices, economic and financial results, sales, among other variables, are causes of concern for investors. Due to their data structure, these financial instruments correspond to time series, which take values or realizations along time and are spaced over time. The previous behavior of the series is used to forecast its value, return and volatility. It must be taken into consideration that forecasting using traditional techniques might result in imprecisions, so it is necessary to forecast using econometric models because of their robustness and precision. These are also known as univariate time series models.Dado que el comportamiento del mercado es volátil, la presente investigación pretende coadyuvar a que inversionistas y organizaciones empresariales puedan realizar pronósticos con certeza y, en consecuencia, con el mínimo error posible, a fin de lograr el éxito en la gestión de sus proyectos y operaciones. Elementos como la tasa de inflación, el tipo de cambio, el precio de las acciones, los resultados económicos financieros, las ventas, entre otras variables, son preocupaciones para los inversionistas. Estos instrumentos financieros, por su estructura de datos, corresponden a las series de tiempo, las cuales toman valores o realizaciones, precisamente, a lo largo del tiempo y, a la vez, están espaciadas cronológicamente. El comportamiento previo es utilizado para pronosticar el valor de la serie, su rendimiento y volatilidad. Y ello debe considerar que pronosticar con las técnicas tradicionales tiene riesgos de imprecisión, por lo que es necesario hacerlo con modelos econométricos por su robustez y precisión, también conocidos como modelos univariados de series de tiempo.Facultad de Ingeniería Industrial, Universidad Nacional Mayor de San Marcos2020-10-15info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdftext/htmlaudio/mpegaudio/mpeghttps://revistasinvestigacion.unmsm.edu.pe/index.php/idata/article/view/1650410.15381/idata.v23i1.16504Industrial Data; Vol. 23 No. 1 (2020); 207-228Industrial Data; Vol. 23 Núm. 1 (2020); 207-2281810-99931560-9146reponame:Revistas - Universidad Nacional Mayor de San Marcosinstname:Universidad Nacional Mayor de San Marcosinstacron:UNMSMspaenghttps://revistasinvestigacion.unmsm.edu.pe/index.php/idata/article/view/16504/15874https://revistasinvestigacion.unmsm.edu.pe/index.php/idata/article/view/16504/15932https://revistasinvestigacion.unmsm.edu.pe/index.php/idata/article/view/16504/15976https://revistasinvestigacion.unmsm.edu.pe/index.php/idata/article/view/16504/15977Derechos de autor 2020 Wilfredo Bazán Ramírezhttps://creativecommons.org/licenses/by-nc-sa/4.0info:eu-repo/semantics/openAccessoai:ojs.csi.unmsm:article/165042020-10-26T11:19:11Z |
dc.title.none.fl_str_mv |
Basics for Forecasting a Stationary Time Series Using Information from Its Past Fundamentos para pronosticar una serie de tiempo estacionaria con información de su propio pasado |
title |
Basics for Forecasting a Stationary Time Series Using Information from Its Past |
spellingShingle |
Basics for Forecasting a Stationary Time Series Using Information from Its Past Bazán Ramírez, Wilfredo time series stationarity unit root white noise variance series de tiempo estacionariedad raíz unitaria ruido blanco varianza |
title_short |
Basics for Forecasting a Stationary Time Series Using Information from Its Past |
title_full |
Basics for Forecasting a Stationary Time Series Using Information from Its Past |
title_fullStr |
Basics for Forecasting a Stationary Time Series Using Information from Its Past |
title_full_unstemmed |
Basics for Forecasting a Stationary Time Series Using Information from Its Past |
title_sort |
Basics for Forecasting a Stationary Time Series Using Information from Its Past |
dc.creator.none.fl_str_mv |
Bazán Ramírez, Wilfredo |
author |
Bazán Ramírez, Wilfredo |
author_facet |
Bazán Ramírez, Wilfredo |
author_role |
author |
dc.subject.none.fl_str_mv |
time series stationarity unit root white noise variance series de tiempo estacionariedad raíz unitaria ruido blanco varianza |
topic |
time series stationarity unit root white noise variance series de tiempo estacionariedad raíz unitaria ruido blanco varianza |
description |
Since market behavior is volatile, this research intends to help investors and business organizations make forecasts with certainty and, as a consequence, with the least possible error in order to succeed in the management of their projects and operations. Elements such as inflation rate, exchange rate, stock prices, economic and financial results, sales, among other variables, are causes of concern for investors. Due to their data structure, these financial instruments correspond to time series, which take values or realizations along time and are spaced over time. The previous behavior of the series is used to forecast its value, return and volatility. It must be taken into consideration that forecasting using traditional techniques might result in imprecisions, so it is necessary to forecast using econometric models because of their robustness and precision. These are also known as univariate time series models. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-10-15 |
dc.type.none.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.none.fl_str_mv |
https://revistasinvestigacion.unmsm.edu.pe/index.php/idata/article/view/16504 10.15381/idata.v23i1.16504 |
url |
https://revistasinvestigacion.unmsm.edu.pe/index.php/idata/article/view/16504 |
identifier_str_mv |
10.15381/idata.v23i1.16504 |
dc.language.none.fl_str_mv |
spa eng |
language |
spa eng |
dc.relation.none.fl_str_mv |
https://revistasinvestigacion.unmsm.edu.pe/index.php/idata/article/view/16504/15874 https://revistasinvestigacion.unmsm.edu.pe/index.php/idata/article/view/16504/15932 https://revistasinvestigacion.unmsm.edu.pe/index.php/idata/article/view/16504/15976 https://revistasinvestigacion.unmsm.edu.pe/index.php/idata/article/view/16504/15977 |
dc.rights.none.fl_str_mv |
Derechos de autor 2020 Wilfredo Bazán Ramírez https://creativecommons.org/licenses/by-nc-sa/4.0 info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Derechos de autor 2020 Wilfredo Bazán Ramírez https://creativecommons.org/licenses/by-nc-sa/4.0 |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf text/html audio/mpeg audio/mpeg |
dc.publisher.none.fl_str_mv |
Facultad de Ingeniería Industrial, Universidad Nacional Mayor de San Marcos |
publisher.none.fl_str_mv |
Facultad de Ingeniería Industrial, Universidad Nacional Mayor de San Marcos |
dc.source.none.fl_str_mv |
Industrial Data; Vol. 23 No. 1 (2020); 207-228 Industrial Data; Vol. 23 Núm. 1 (2020); 207-228 1810-9993 1560-9146 reponame:Revistas - Universidad Nacional Mayor de San Marcos instname:Universidad Nacional Mayor de San Marcos instacron:UNMSM |
instname_str |
Universidad Nacional Mayor de San Marcos |
instacron_str |
UNMSM |
institution |
UNMSM |
reponame_str |
Revistas - Universidad Nacional Mayor de San Marcos |
collection |
Revistas - Universidad Nacional Mayor de San Marcos |
repository.name.fl_str_mv |
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repository.mail.fl_str_mv |
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1795238303375556608 |
score |
13.871978 |
Nota importante:
La información contenida en este registro es de entera responsabilidad de la institución que gestiona el repositorio institucional donde esta contenido este documento o set de datos. El CONCYTEC no se hace responsable por los contenidos (publicaciones y/o datos) accesibles a través del Repositorio Nacional Digital de Ciencia, Tecnología e Innovación de Acceso Abierto (ALICIA).
La información contenida en este registro es de entera responsabilidad de la institución que gestiona el repositorio institucional donde esta contenido este documento o set de datos. El CONCYTEC no se hace responsable por los contenidos (publicaciones y/o datos) accesibles a través del Repositorio Nacional Digital de Ciencia, Tecnología e Innovación de Acceso Abierto (ALICIA).