Pronósticos de variables climatológicas mediante los modelos de punto de cambio y Holt-Winters
DOI:
https://doi.org/10.61799/2216-0388.986Palabras clave:
holt-winters, precipitación, pronósticos, punto de cambioResumen
En este estudio se analiza una serie de tiempo con datos históricos diarios desde enero 1 de 1989 hasta el 31 de diciembre del año 2021 de la variable precipitación con un total de 12053 observaciones, estos datos son obtenidos a partir de la estación climatológica Tunjuelito. Para la investigación se tuvieron en cuenta los registros de la variable “precipitación”, el objetivo fue analizar las tendencias, utilizar los datos hasta el 31 de diciembre de 2020 para estimar un pronóstico para el año 2021 método de Holt-Winters y el modelo de punto de cambio, se comparan los datos observados con los pronosticados. Por último, se realizan pruebas estadísticas para contrastar el grado de similitud de los datos obtenidos a partir de los pronósticos con los datos observados arrojados por la estación. Los resultados demuestran que los pronósticos obtenidos con el modelo de punto de cambio evidencian una mayor precisión y se ajusta relativamente bien a los datos observados. Sin embargo este estudio se considera preliminar y para que los resultados puedan ser considerados como concluyentes de deben aplicar a una cantidad significativa de series de tiempo de variables meteorológicas.
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A. Esquivel et al., “Predictability of seasonal precipitation across major crop growing areas in Colombia,” Clim. Serv., vol. 12, no. March, pp. 36–47, 2018, doi: 10.1016/j.cliser.2018.09.001.
H. Feidas, C. Noulopoulou, T. Makrogiannis, and E. Bora-Senta, “Trend analysis of precipitation time series in Greece and their relationship with circulation using surface and satellite data: 1955-2001,” Theor. Appl. Climatol., vol. 87, no. 1–4, pp. 155–177, 2007, doi: 10.1007/s00704-006-0200-5.
P. T. Nastos, A. G. Paliatsos, K. V. Koukouletsos, I. K. Larissi, and K. P. Moustris, “Artificial neural networks modeling for forecasting the maximum daily total precipitation at Athens, Greece,” Atmos. Res., vol. 144, pp. 141–150, 2014, doi: 10.1016/j.atmosres.2013.11.013
T. Li, C. Qiao, L. Wang, J. Chen, and Y. Ren, “An Algorithm for Precipitation Correction in Flood Season Based on Dendritic Neural Network,” Front. Plant Sci., vol. 13, no. May, pp. 1–13, 2022, doi: 10.3389/fpls.2022.862558
T. T. H. Phan, É. Poisson Caillault, and A. Bigand, “Comparative study on univariate forecasting methods for meteorological time series,” Eur. Signal Process. Conf., vol. 2018-Septe, pp. 2380–2384, 2018, doi: 10.23919/EUSIPCO.2018.8553576
A. J. M. Jacobs and N. Maat, “Numerical guidance methods for decision support in aviation meteorological forecasting,” Weather Forecast., vol. 20, no. 1, pp. 82–100, 2005, doi: 10.1175/WAF-827.1
J. Arroyo and C. Maté, “Forecasting histogram time series with k-nearest neighbours methods,” Int. J. Forecast., vol. 25, no. 1, pp. 192–207, 2009, doi: 10.1016/j.ijforecast.2008.07.003.
M. Heydari, H. B. Ghadim, M. Rashidi, and M. Noori, “Application of holt-winters time series models for predicting climatic parameters (Case study: Robat Garah-Bil station, Iran),” Polish J. Environ. Stud., vol. 29, no. 1, pp. 617–627, 2020, doi: 10.15244/pjoes/100496
M. V. Zhitlukhin and W. T. Ziemba, “Exit strategies in bubble-like markets using a changepoint model,” Quant. Financ. Lett., vol. 4, no. 1, pp. 47–52, 2016, doi: 10.1080/21649502.2015.1165918
M. J. Lenardon and A. Amirdjanova, “Interaction between stock indices via changepoint analysis,” pp. 573–586, 2006, doi: 10.1002/asmb
D. Barry and J. A. Hartigan, “A Bayesian Analysis for Change Point Problems,” J. Am. Stat. Assoc., vol. 88, no. 421, p. 309, 1993, doi: 10.2307/2290726
R. Cmejla, J. Rusz, P. Bergl, and J. Vokral, “Bayesian changepoint detection for the automatic assessment of fluency and articulatory disorders,” Speech Commun., vol. 55, no. 1, pp. 178–189, 2013, doi: https://doi.org/10.1016/j.specom.2012.08.003
C. Jeong and J. Kim, “Bayesian multiple structural change-points estimation in time series models with genetic algorithm,” J. Korean Stat. Soc., vol. 42, no. 4, pp. 459–468, Dec. 2013, doi: 10.1016/j.jkss.2013.02.001
S. Liu, M. Yamada, N. Collier, and M. Sugiyama, “Change-point detection in time-series data by relative density-ratio estimation.,” Neural Netw., vol. 43, pp. 72–83, Jul. 2013, doi: 10.1016/j.neunet.2013.01.012
S. Li and R. Lund, “Multiple Changepoint Detection via Genetic Algorithms,” J. Clim., vol. 25, no. 2, pp. 674–686, Jan. 2012, doi: 10.1175/2011JCLI4055.1
J. Chen and K. Gupta, Parametric Statistical Change Point Analysis, 2nd ed. Boston: Birkhäuser, Springer, 2012
F. A. Alawadhi and D. Alhulail, “Bayesian change points analysis for earthquakes body wave magnitude,” J. Appl. Stat., vol. 43, no. 9, pp. 1567–1582, 2016, doi: 10.1080/02664763.2015.1117585
D. Picard, “Testing and estimating change-points in time series,” vol. 17, no. 4, pp. 841–867, 2013
P. J. Plummer and B.S.E., “Decting Change–Points in a compound Poisson,” 2012
E. L. Lehmann, “On likelihood ratio tests,” Optimality, vol. 49, no. 2006, pp. 1–8, 2006, doi: 10.1214/074921706000000356
T. Wang, W. Tian, and W. Ning, “Likelihood ratio test change-point detection in the skew slash distribution,” Commun. Stat. Simul. Comput., vol. 0, no. 0, pp. 1–13, 2020, doi: 10.1080/03610918.2020.1755869
Y. Zhou, L. Fu, and B. Zhang, “Two non parametric methods for change-point detection in distribution,” Commun. Stat. - Theory Methods, vol. 46, no. 6, pp. 2801–2815, 2017, doi: 10.1080/03610926.2015.1048891
J. Vanegas and F. Vásquez, “[Multivariate Adaptive Regression Splines (MARS), an alternative for the analysis of time series].,” Gac. Sanit., no. xx, pp. 4–6, Dec. 2016, doi: 10.1016/j.gaceta.2016.10.003.
V. Grech and N. Calleja, “WASP (Write a Scientific Paper): Parametric vs. non-parametric tests,” Early Hum. Dev., no. xxxx, pp. 1–2, 2018, doi: 10.1016/j.earlhumdev.2018.04.014
S. Herrmann, H. Schwender, K. Ickstadt, and P. Müller, “A Bayesian changepoint analysis of ChIP-Seq data of Lamin B,” Biochim. Biophys. Acta - Proteins Proteomics, vol. 1844, no. 1 PART A, pp. 138–144, 2014, doi: 10.1016/j.bbapap.2013.09.001
L. O. Mesa, M. Rivera, and J. A. Romero, “Descripción general de la Inferencia Bayesiana y sus aplicaciones en los procesos de gestión,” La Simulación al Serv. la Acad., vol. 2, pp. 1–28, 2011
A. Contreras Juárez, C. Atziry Zuñiga, J. L. Martínez Flores, and D. Sánchez Partida, “Análisis de series de tiempo en el pronóstico de la demanda de almacenamiento de productos perecederos,” Estud. Gerenciales, vol. 32, no. 141, pp. 387–396, Oct. 2016, doi: 10.1016/j.estger.2016.11.002
G. Masterton, “What to do with a forecast?,” Synthese, vol. 191, no. 8, pp. 1881–1907, 2014, doi: 10.1007/s11229-013-0384-z
R. Ballou, Logística: Administración de la cadena de suministro, 5ta Ed. México, 2004
M. Mas-Machuca, M. Sainz, and C. Martinez-Costa, “A review of forecasting models for new products,” Intang. Cap., vol. 10, no. 1, pp. 1–25, 2014, doi: 10.3926/ic.482
D. J. Eck, “Bootstrapping for multivariate linear regression models,” Stat. Probab. Lett., vol. 134, pp. 141–149, 2018, doi: 10.1016/j.spl.2017.11.001
L. Ferbar Tratar, B. Mojškerc, and A. Toman, “Demand forecasting with four-parameter exponential smoothing,” Int. J. Prod. Econ., vol. 181, pp. 162–173, 2016, doi: 10.1016/j.ijpe.2016.08.004
J. W. Taylor, “Multi-item sales forecasting with total and split exponential smoothing,” J. Oper. Res. Soc., vol. 62, no. 3, pp. 555–563, 2011, doi: 10.1057/jors.2010.95
E. M. de Oliveira and F. L. Cyrino Oliveira, “Forecasting mid-long term electric energy consumption through bagging ARIMA and exponential smoothing methods,” Energy, vol. 144, pp. 776–788, 2018, doi: 10.1016/j.energy.2017.12.049
B. Heizer, J., & Render, Principios de administración de operaciones. Pearson Educación., 7ma Ed. Pearson, 2004
L. Frías-Paredes, F. Mallor, M. Gastón-Romeo, and T. León, “Dynamic mean absolute error as new measure for assessing forecasting errors,” Energy Convers. Manag., vol. 162, no. December 2017, pp. 176–188, 2018, doi: 10.1016/j.enconman.2018.02.030
M. V. Shcherbakov, A. Brebels, N. L. Shcherbakova, A. P. Tyukov, T. A. Janovsky, and V. A. evich Kamaev, “A survey of forecast error measures,” World Appl. Sci. J., vol. 24, no. 24, pp. 171–176, 2013, doi: 10.5829/idosi.wasj.2013.24.itmies.80032
“Precipitation,” Adv. Remote Sens., pp. 621–647, Jan. 2020, doi: 10.1016/B978-0-12-815826-5.00016-7
S. Michaelides, V. Levizzani, E. Anagnostou, P. Bauer, T. Kasparis, and J. E. Lane, “Precipitation: Measurement, remote sensing, climatology and modeling,” Atmos. Res., vol. 94, no. 4, pp. 512–533, 2009, doi: 10.1016/j.atmosres.2009.08.017
C. Kummerow, W. Barnes, T. Kozu, J. Shiue, and J. Simpson, “The Tropical Rainfall Measuring Mission (TRMM) sensor package,” J. Atmos. Ocean. Technol., vol. 15, no. 3, pp. 809–817, 1998
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Publicado
2021-09-01
Cómo citar
Valderrama-Balaguera, J. C., Castro-Silva, H. F., & Dávila Carrillo, C. A. (2021). Pronósticos de variables climatológicas mediante los modelos de punto de cambio y Holt-Winters. Mundo FESC, 11(S2), 337–352. https://doi.org/10.61799/2216-0388.986
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Articulos
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Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial 4.0.
