Basics for Forecasting a Stationary Time Series Using Information from Its Past

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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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Detalles Bibliográficos
Autor: Bazán Ramírez, Wilfredo
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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spelling 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
repository.mail.fl_str_mv
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score 13.871978
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