Pronósticos en series de tiempo no lineales: aplicación del modelo TSARX y comparación con modelos para datos estacionales
ilustraciones
- Autores:
-
Arbeláez Quintero, Sebastián
- Tipo de recurso:
- Fecha de publicación:
- 2022
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/83875
- Palabra clave:
- 510 - Matemáticas::519 - Probabilidades y matemáticas aplicadas
No linealidad
Modelos de umbral
Estacionalidad
Pronósticos
Economía
Non-linearity
Threshold models
Seasonality
Forecast
Economics
Previsión
Inferencia estadística
Series temporales
Forecasting
Statistical inference
Time series
- Rights
- openAccess
- License
- Atribución-NoComercial-SinDerivadas 4.0 Internacional
| id |
UNACIONAL2_e713eee3ac0977316d07f0fead023891 |
|---|---|
| oai_identifier_str |
oai:repositorio.unal.edu.co:unal/83875 |
| network_acronym_str |
UNACIONAL2 |
| network_name_str |
Universidad Nacional de Colombia |
| repository_id_str |
|
| dc.title.spa.fl_str_mv |
Pronósticos en series de tiempo no lineales: aplicación del modelo TSARX y comparación con modelos para datos estacionales |
| dc.title.translated.spa.fl_str_mv |
Non-linear time series forecasting: application of the TSARX model and comparison with models for seasonal data |
| title |
Pronósticos en series de tiempo no lineales: aplicación del modelo TSARX y comparación con modelos para datos estacionales |
| spellingShingle |
Pronósticos en series de tiempo no lineales: aplicación del modelo TSARX y comparación con modelos para datos estacionales 510 - Matemáticas::519 - Probabilidades y matemáticas aplicadas No linealidad Modelos de umbral Estacionalidad Pronósticos Economía Non-linearity Threshold models Seasonality Forecast Economics Previsión Inferencia estadística Series temporales Forecasting Statistical inference Time series |
| title_short |
Pronósticos en series de tiempo no lineales: aplicación del modelo TSARX y comparación con modelos para datos estacionales |
| title_full |
Pronósticos en series de tiempo no lineales: aplicación del modelo TSARX y comparación con modelos para datos estacionales |
| title_fullStr |
Pronósticos en series de tiempo no lineales: aplicación del modelo TSARX y comparación con modelos para datos estacionales |
| title_full_unstemmed |
Pronósticos en series de tiempo no lineales: aplicación del modelo TSARX y comparación con modelos para datos estacionales |
| title_sort |
Pronósticos en series de tiempo no lineales: aplicación del modelo TSARX y comparación con modelos para datos estacionales |
| dc.creator.fl_str_mv |
Arbeláez Quintero, Sebastián |
| dc.contributor.advisor.spa.fl_str_mv |
Calderón Villanueva, Sergio Alejandro |
| dc.contributor.author.spa.fl_str_mv |
Arbeláez Quintero, Sebastián |
| dc.subject.ddc.spa.fl_str_mv |
510 - Matemáticas::519 - Probabilidades y matemáticas aplicadas |
| topic |
510 - Matemáticas::519 - Probabilidades y matemáticas aplicadas No linealidad Modelos de umbral Estacionalidad Pronósticos Economía Non-linearity Threshold models Seasonality Forecast Economics Previsión Inferencia estadística Series temporales Forecasting Statistical inference Time series |
| dc.subject.proposal.spa.fl_str_mv |
No linealidad Modelos de umbral Estacionalidad Pronósticos Economía |
| dc.subject.proposal.eng.fl_str_mv |
Non-linearity Threshold models Seasonality Forecast Economics |
| dc.subject.unesco.spa.fl_str_mv |
Previsión Inferencia estadística Series temporales |
| dc.subject.unesco.eng.fl_str_mv |
Forecasting Statistical inference Time series |
| description |
ilustraciones |
| publishDate |
2022 |
| dc.date.issued.none.fl_str_mv |
2022 |
| dc.date.accessioned.none.fl_str_mv |
2023-05-25T20:33:21Z |
| dc.date.available.none.fl_str_mv |
2023-05-25T20:33:21Z |
| dc.type.spa.fl_str_mv |
Trabajo de grado - Maestría |
| dc.type.driver.spa.fl_str_mv |
info:eu-repo/semantics/masterThesis |
| dc.type.version.spa.fl_str_mv |
info:eu-repo/semantics/acceptedVersion |
| dc.type.content.spa.fl_str_mv |
Text |
| dc.type.redcol.spa.fl_str_mv |
http://purl.org/redcol/resource_type/TM |
| status_str |
acceptedVersion |
| dc.identifier.uri.none.fl_str_mv |
https://repositorio.unal.edu.co/handle/unal/83875 |
| dc.identifier.instname.spa.fl_str_mv |
Universidad Nacional de Colombia |
| dc.identifier.reponame.spa.fl_str_mv |
Repositorio Institucional Universidad Nacional de Colombia |
| dc.identifier.repourl.spa.fl_str_mv |
https://repositorio.unal.edu.co/ |
| url |
https://repositorio.unal.edu.co/handle/unal/83875 https://repositorio.unal.edu.co/ |
| identifier_str_mv |
Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia |
| dc.language.iso.spa.fl_str_mv |
spa |
| language |
spa |
| dc.relation.references.spa.fl_str_mv |
Bates, J. M., y Granger, C. W. J. (1969). The combination of forecasts. Journal of the Operational Research Society, 20 (4), 451-468. Box, G., y Jenkins, G. M. (1991). Time Series Analysis: Forecasting and Control. PrenticeHall. Brown, R. G. (1959). Statistical forecasting for inventory control. McGraw/Hill. Brown, R. L., Durbin, J., y Evans, J. M. (1975). Techniques for testing the constancy of regression relationships over time. Journal of the Royal Statistical Society. Series B (Methodological), 37 (2), 149-192. Cao, W., Zhu, W., Wang, W., Demazeau, Y., y Zhang, C. (2020). A deep coupled lstm approach for usd/cny exchange rate forecasting. IEEE Intelligent Systems, 35 (2), 43-53. doi: 10.1109/MIS.2020.2977283 Congodon, P. (2007). Bayesian statistical modelling. Southern Gate, Chichester: John Wiley & Sons. DANE. (2015). Metodología general gran encuesta integrada de hogares geih. DANE. (2016). Metodología general indicador de seguimiento a la economía ise De Gooijer, J. G., y Vidiella-i Anguera, A. (2003). Nonlinear stochastic inflation modelling using seasetars. Insurance: Mathematics and Economics, 32 ((1)), 27–36. Dellaportas, P., Foster, J. J., y Ntzoufras, I. (2002). On bayesian model and variable selection using mcmc. Statistics and Computing, 12 ((1)), 27–36. Diebold, F., y Mariano, R. (1995). Comparing predictive accuracy. Journal of Business & Economic Statistics, 13 ((3)), 253–263. Dixon, M. F., Halperin, I., y Bilokon, P. (2020). Machine learning in finance from theory to practice. Switzerland: SP. Edgerton, D., y Wells, C. (1994). Critical values for the cusumsq statistic in medium and large sized samples. Oxford Bulletin of Economics and Statistics, 56 (3), 355-365. Etuk, E. H. (2012). Predicting inflation rates of nigeria using a seasonal box-jenkins model. Journal of Statistical and Econometric Methods, 1-3 , 27-37. González, J., y Nieto, H., F. (2020). Bayesian analysis of multiplicative seasonal threshold autoregressive processes. Revista Colombiana de Estadística - Applied Statistics, 43 (2), 251–285. Hansen, B. E. (2011). Threshold autoregression in economics. Statistics and their interface, 4 , 123–127. Hochreiter, S., y Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735-1780. Holt, C. (2004). Forecasting seasonals and trends by exponentially weighted averages. En Onr memorandum no. 52. Hyndman, R., y Athanasopoulos, G. (2021). Forecasting: principles and practice (3rd ed.). Melbourne: OTexts. Jiehua, L., Chao, W., Wei, G., y Qiumin, Z. (2021). An economic forecasting method based on the lightgbm-optimized lstm and time-series model. Neural Computation, 2021 , 1-10. Kryzanowski, L., y Zhang, H. (1992). Economic forces and seasonality in security returns. Review of Quantitative Finance and Accounting, 2 , 227-244. Luetkepohl, H., y Xu, F. (2012). Forecasting annual inflation with seasonal monthly data: Using levels versus logs of the underlying price index. Journal of Statistical and Econometric Methods, 1-3 , 27-37. Nieto, F. H. (2005). Modeling bivariate threshold autoregressive processes in the presence of missing data. Communications in Statistics. Theory and Methods, 34 , 905–930. Nieto, F. H. (2008). Forecasting with univariate tar models. Statistical Methodology, 5 , 263–276. Peirano, R., Kristjanpoller, W., y Minutolo, M. (2021). Forecasting inflation in latin american countries using a sarima–lstm combination. Neural Computation, 25 , 10851–10862. Peña, D., y Tsay, R. S. (2021). Statistical Learning for Big Dependent Data. Wiley Series in Probability and Statistics Siami-Namini, S., Tavakoli, N., y Siami Namin, A. (2018). A comparison of arima and lstm in forecasting time series. En 2018 17th ieee international conference on machine learning and applications (icmla) (p. 1394-1401). doi: 10.1109/ICMLA.2018.00227 Tong, H., y Chen, C. H. (1978). Pattern recognition and signal processing. Netherlands: Springer. Tong, H., y Lim, K. S. (1980). Threshold autoregression, limit cycles, and cyclical data. Journal of the Royal Statistical Society, Series B, 42 ((3)), 245–292. Tsay, R. S. (1998). Testing and modeling multivariate threshold models. Journal of the American Statistical Association, 93 (443), 1188–1202. Urrutia, J. D., Rivera, C. I., Quite, J. A., Belamide, J. A., y Quinto, J. Q. (2014). Application of seasonal autoregressive integrated moving average (sarima) in modeling and forecasting philippine real gross domestic product. European Academic Research, II, 11247-11297. Vaca, P. A. (2018). Analysis of the forecasting performance of the threshold autorregresive model (Tesis de Master no publicada). Universidad Nacional de Colombia. Winters, P. R. (1960). Forecasting sales by exponentially weighted moving averages. Management Science, 6(3), 324-342. Zhang, G., y Qi, M. (2005). Neural network forecasting for seasonal and trend time series. EJOR, 160 (2), 501-514. |
| dc.rights.coar.fl_str_mv |
http://purl.org/coar/access_right/c_abf2 |
| dc.rights.license.spa.fl_str_mv |
Atribución-NoComercial-SinDerivadas 4.0 Internacional |
| dc.rights.uri.spa.fl_str_mv |
http://creativecommons.org/licenses/by-nc-nd/4.0/ |
| dc.rights.accessrights.spa.fl_str_mv |
info:eu-repo/semantics/openAccess |
| rights_invalid_str_mv |
Atribución-NoComercial-SinDerivadas 4.0 Internacional http://creativecommons.org/licenses/by-nc-nd/4.0/ http://purl.org/coar/access_right/c_abf2 |
| eu_rights_str_mv |
openAccess |
| dc.format.extent.spa.fl_str_mv |
xiii, 168 páginas |
| dc.format.mimetype.spa.fl_str_mv |
application/pdf |
| dc.publisher.spa.fl_str_mv |
Universidad Nacional de Colombia |
| dc.publisher.program.spa.fl_str_mv |
Bogotá - Ciencias - Maestría en Ciencias - Estadística |
| dc.publisher.faculty.spa.fl_str_mv |
Facultad de Ciencias |
| dc.publisher.place.spa.fl_str_mv |
Bogotá, Colombia |
| dc.publisher.branch.spa.fl_str_mv |
Universidad Nacional de Colombia - Sede Bogotá |
| institution |
Universidad Nacional de Colombia |
| bitstream.url.fl_str_mv |
https://repositorio.unal.edu.co/bitstream/unal/83875/1/license.txt https://repositorio.unal.edu.co/bitstream/unal/83875/2/1030623144.2022.pdf https://repositorio.unal.edu.co/bitstream/unal/83875/3/1030623144.2022.pdf.jpg |
| bitstream.checksum.fl_str_mv |
eb34b1cf90b7e1103fc9dfd26be24b4a d3a36ee4a3f0e79ad430c96d76512905 cd32e1bd919c6654b6611fbb7d950945 |
| bitstream.checksumAlgorithm.fl_str_mv |
MD5 MD5 MD5 |
| repository.name.fl_str_mv |
Repositorio Institucional Universidad Nacional de Colombia |
| repository.mail.fl_str_mv |
repositorio_nal@unal.edu.co |
| _version_ |
1814089345602158592 |
| spelling |
Atribución-NoComercial-SinDerivadas 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Calderón Villanueva, Sergio Alejandro4435821363acfcc5a0b97c50464db9d4Arbeláez Quintero, Sebastián41e78b8f8b2fc68c04952ce1b6dff1d62023-05-25T20:33:21Z2023-05-25T20:33:21Z2022https://repositorio.unal.edu.co/handle/unal/83875Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustracionesLa introducción de los modelos TAR en el análisis económico ha permitido capturar el comportamiento no lineal, usualmente observado en este tipo de series de tiempo. Adicional a la no linealidad, las series de tiempo económicas pueden presentar comportamientos estacionales cuyo análisis podría llevar a conclusiones más acordes con la realidad. Por otro lado, realizar pronósticos acertados sobre los valores que una serie de tiempo tomará, es un tema importante en economía, por lo que un sector de la literatura se ha encargado de comparar la precisión de los pronósticos generados a partir de diferentes tipos de modelos. En este trabajo se compara la precisión de los pronósticos obtenidos al ajustar el modelo Multiplicative Seasonal Threshold Autoregressive with exogenous input - TSARX, con respecto a otros modelos usualmente empleados en la literatura. El modelo TSARX permite capturar el comportamiento estacional multiplicativo de las series de tiempo para explicar el proceso de interés. (Texto tomado de la fuente).Introduction of TAR models in economic analysis has allowed to capture the non linear behavior usually observed this kind of time series. In addition to non linearity, economic time series may exhibit seasonal patterns whose analysis may carry to conclusions more in line with reality. Otherwise, accurate forecasting for time series its a relevant topic in economics, thus a part of literature has been in charge to compare the accuracy of forecasts generated by different types of models. In this work, accuracy of forecast obtained by adjusting Multiplicative Seasonal Threshold Autorregessive with exogenous input - TSARX models, is compared with those obtained by adjusting other models usually used in literature. TSARX model allows to capture time series multiplicative seasonality to explain the process of interest.Incluye anexosMaestríaMagíster en Ciencias - Estadísticaxiii, 168 páginasapplication/pdfspaUniversidad Nacional de ColombiaBogotá - Ciencias - Maestría en Ciencias - EstadísticaFacultad de CienciasBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá510 - Matemáticas::519 - Probabilidades y matemáticas aplicadasNo linealidadModelos de umbralEstacionalidadPronósticosEconomíaNon-linearityThreshold modelsSeasonalityForecastEconomicsPrevisiónInferencia estadísticaSeries temporalesForecastingStatistical inferenceTime seriesPronósticos en series de tiempo no lineales: aplicación del modelo TSARX y comparación con modelos para datos estacionalesNon-linear time series forecasting: application of the TSARX model and comparison with models for seasonal dataTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMBates, J. M., y Granger, C. W. J. (1969). The combination of forecasts. Journal of the Operational Research Society, 20 (4), 451-468.Box, G., y Jenkins, G. M. (1991). Time Series Analysis: Forecasting and Control. PrenticeHall.Brown, R. G. (1959). Statistical forecasting for inventory control. McGraw/Hill.Brown, R. L., Durbin, J., y Evans, J. M. (1975). Techniques for testing the constancy of regression relationships over time. Journal of the Royal Statistical Society. Series B (Methodological), 37 (2), 149-192.Cao, W., Zhu, W., Wang, W., Demazeau, Y., y Zhang, C. (2020). A deep coupled lstm approach for usd/cny exchange rate forecasting. IEEE Intelligent Systems, 35 (2), 43-53. doi: 10.1109/MIS.2020.2977283Congodon, P. (2007). Bayesian statistical modelling. Southern Gate, Chichester: John Wiley & Sons.DANE. (2015). Metodología general gran encuesta integrada de hogares geih.DANE. (2016). Metodología general indicador de seguimiento a la economía iseDe Gooijer, J. G., y Vidiella-i Anguera, A. (2003). Nonlinear stochastic inflation modelling using seasetars. Insurance: Mathematics and Economics, 32 ((1)), 27–36.Dellaportas, P., Foster, J. J., y Ntzoufras, I. (2002). On bayesian model and variable selection using mcmc. Statistics and Computing, 12 ((1)), 27–36.Diebold, F., y Mariano, R. (1995). Comparing predictive accuracy. Journal of Business & Economic Statistics, 13 ((3)), 253–263.Dixon, M. F., Halperin, I., y Bilokon, P. (2020). Machine learning in finance from theory to practice. Switzerland: SP.Edgerton, D., y Wells, C. (1994). Critical values for the cusumsq statistic in medium and large sized samples. Oxford Bulletin of Economics and Statistics, 56 (3), 355-365.Etuk, E. H. (2012). Predicting inflation rates of nigeria using a seasonal box-jenkins model. Journal of Statistical and Econometric Methods, 1-3 , 27-37.González, J., y Nieto, H., F. (2020). Bayesian analysis of multiplicative seasonal threshold autoregressive processes. Revista Colombiana de Estadística - Applied Statistics, 43 (2), 251–285.Hansen, B. E. (2011). Threshold autoregression in economics. Statistics and their interface, 4 , 123–127.Hochreiter, S., y Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735-1780.Holt, C. (2004). Forecasting seasonals and trends by exponentially weighted averages. En Onr memorandum no. 52.Hyndman, R., y Athanasopoulos, G. (2021). Forecasting: principles and practice (3rd ed.). Melbourne: OTexts.Jiehua, L., Chao, W., Wei, G., y Qiumin, Z. (2021). An economic forecasting method based on the lightgbm-optimized lstm and time-series model. Neural Computation, 2021 , 1-10.Kryzanowski, L., y Zhang, H. (1992). Economic forces and seasonality in security returns. Review of Quantitative Finance and Accounting, 2 , 227-244.Luetkepohl, H., y Xu, F. (2012). Forecasting annual inflation with seasonal monthly data: Using levels versus logs of the underlying price index. Journal of Statistical and Econometric Methods, 1-3 , 27-37.Nieto, F. H. (2005). Modeling bivariate threshold autoregressive processes in the presence of missing data. Communications in Statistics. Theory and Methods, 34 , 905–930.Nieto, F. H. (2008). Forecasting with univariate tar models. Statistical Methodology, 5 , 263–276.Peirano, R., Kristjanpoller, W., y Minutolo, M. (2021). Forecasting inflation in latin american countries using a sarima–lstm combination. Neural Computation, 25 , 10851–10862.Peña, D., y Tsay, R. S. (2021). Statistical Learning for Big Dependent Data. Wiley Series in Probability and StatisticsSiami-Namini, S., Tavakoli, N., y Siami Namin, A. (2018). A comparison of arima and lstm in forecasting time series. En 2018 17th ieee international conference on machine learning and applications (icmla) (p. 1394-1401). doi: 10.1109/ICMLA.2018.00227Tong, H., y Chen, C. H. (1978). Pattern recognition and signal processing. Netherlands: Springer.Tong, H., y Lim, K. S. (1980). Threshold autoregression, limit cycles, and cyclical data. Journal of the Royal Statistical Society, Series B, 42 ((3)), 245–292.Tsay, R. S. (1998). Testing and modeling multivariate threshold models. Journal of the American Statistical Association, 93 (443), 1188–1202.Urrutia, J. D., Rivera, C. I., Quite, J. A., Belamide, J. A., y Quinto, J. Q. (2014). Application of seasonal autoregressive integrated moving average (sarima) in modeling and forecasting philippine real gross domestic product. European Academic Research, II, 11247-11297.Vaca, P. A. (2018). Analysis of the forecasting performance of the threshold autorregresive model (Tesis de Master no publicada). Universidad Nacional de Colombia.Winters, P. R. (1960). Forecasting sales by exponentially weighted moving averages. Management Science, 6(3), 324-342.Zhang, G., y Qi, M. (2005). Neural network forecasting for seasonal and trend time series. EJOR, 160 (2), 501-514.EstudiantesInvestigadoresPúblico generalLICENSElicense.txtlicense.txttext/plain; charset=utf-85879https://repositorio.unal.edu.co/bitstream/unal/83875/1/license.txteb34b1cf90b7e1103fc9dfd26be24b4aMD51ORIGINAL1030623144.2022.pdf1030623144.2022.pdfTesis de Maestría en Ciencias - Estadísticaapplication/pdf1873337https://repositorio.unal.edu.co/bitstream/unal/83875/2/1030623144.2022.pdfd3a36ee4a3f0e79ad430c96d76512905MD52THUMBNAIL1030623144.2022.pdf.jpg1030623144.2022.pdf.jpgGenerated Thumbnailimage/jpeg4760https://repositorio.unal.edu.co/bitstream/unal/83875/3/1030623144.2022.pdf.jpgcd32e1bd919c6654b6611fbb7d950945MD53unal/83875oai:repositorio.unal.edu.co:unal/838752023-08-06 23:03:56.034Repositorio Institucional Universidad Nacional de Colombiarepositorio_nal@unal.edu.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 |