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Electrical & Electronic Engineering

Precipitation forecast estimation applying the change point method and ARIMA

Article: 2340191 | Received 04 Aug 2022, Accepted 03 Apr 2024, Published online: 16 Apr 2024

References

  • Alawadhi, F. A., & Alhulail, D. (2016). Bayesian change points analysis for earthquakes body wave magnitude. Journal of Applied Statistics, 43(9), 1567–1582. https://doi.org/10.1080/02664763.2015.1117585
  • Arroyo, J., & Maté, C. (2009). Forecasting histogram time series with k-nearest neighbours methods. International Journal of Forecasting, 25(1), 192–207. https://doi.org/10.1016/j.ijforecast.2008.07.003
  • Ballou, R. (2004). Logistics: Supply chain management (5th ed.). México.
  • Barry, D., & Hartigan, J. A. (1993). A Bayesian analysis for change point problems. Journal of the American Statistical Association, 88(421), 309. https://doi.org/10.2307/2290726
  • Bourgouin, P. (2000). A method to determine precipitation types. Weather and Forecasting, 15(5), 583–592. https://doi.org/10.1175/1520-0434(2000)015<0583:AMTDPT>2.0.CO;2
  • Chen, J., & Gupta, K. (2012). Parametric statistical change point analysis (2nd ed.). Birkhäuser, Springer.
  • Cmejla, R., Rusz, J., Bergl, P., & Vokral, J. (2013). Bayesian changepoint detection for the automatic assessment of fluency and articulatory disorders. Speech Communication, 55(1), 178–189. https://doi.org/10.1016/j.specom.2012.08.003
  • Contreras Juárez, A., Atziry Zuñiga, C., Martínez Flores, J. L., & Sánchez Partida, D. (2016). Analysis of time series in the forecast of the demand for storage of perishable products. Estud. Gerenciales, 32(141), 387–396. https://doi.org/10.1016/j.estger.2016.11.002.
  • de Oliveira, E. M., & Cyrino Oliveira, F. L. (2018). Forecasting mid-long term electric energy consumption through bagging ARIMA and exponential smoothing methods. Energy, 144, 776–788. https://doi.org/10.1016/j.energy.2017.12.049
  • Eck, D. J. (2018). Bootstrapping for multivariate linear regression models. Statistics & Probability Letters, 134, 141–149. https://doi.org/10.1016/j.spl.2017.11.001
  • Esquivel, A., Llanos-Herrera, L., Agudelo, D., Prager, S. D., Fernandes, K., Rojas, A., Valencia, J. J., & Ramirez-Villegas, J. (2018). Predictability of seasonal precipitation across major crop growing areas in Colombia. Climate Services, 12, 36–47. https://doi.org/10.1016/j.cliser.2018.09.001
  • Feidas, H., Noulopoulou, C., Makrogiannis, T., & Bora-Senta, E. (2007). Trend analysis of precipitation time series in Greece and their relationship with circulation using surface and satellite data: 1955–2001. Theoretical and Applied Climatology, 87(1–4), 155–177. https://doi.org/10.1007/s00704-006-0200-5
  • Ferbar Tratar, L., Mojškerc, B., & Toman, A. (2016). Demand forecasting with four-parameter exponential smoothing. International Journal of Production Economics, 181, 162–173. https://doi.org/10.1016/j.ijpe.2016.08.004
  • Frías-Paredes, L., Mallor, F., Gastón-Romeo, M., & León, T. (2018). Dynamic mean absolute error as new measure for assessing forecasting errors. Energy Convers. Manag, 162, 176–188. https://doi.org/10.1016/j.enconman.2018.02.030
  • Grech, V., & Calleja, N. (2018). WASP (Write a Scientific Paper): Parametric vs. non-parametric tests. Early Human Development, 123, 48–49. https://doi.org/10.1016/j.earlhumdev.2018.04.014
  • Heizer, B., & Render, J. (2004). Operations management (7th ed.). Pearson Education.
  • Herrmann, S., Schwender, H., Ickstadt, K., & Müller, P. (2014). A Bayesian changepoint analysis of ChIP-Seq data of Lamin B. Biochimica et Biophysica Acta, 1844(1 Pt A), 138–144. https://doi.org/10.1016/j.bbapap.2013.09.001.
  • Heydari, M., Ghadim, H. B., Rashidi, M., & Noori, M. (2020). Application of holt-winters time series models for predicting climatic parameters (Case study: Robat Garah-Bil station, Iran. Polish Journal of Environmental Studies, 29(1), 617–627.) https://doi.org/10.15244/pjoes/100496
  • Jacobs, A. J. M., & Maat, N. (2005). Numerical guidance methods for decision support in aviation meteorological forecasting. Weather and Forecasting, 20(1), 82–100. https://doi.org/10.1175/WAF-827.1
  • Jindi, W., Shunlin, L., & Xiaowen, L. (Eds.). (2020). Precipitation. Advanced remote sensing (pp. 621–647). Elsevier Science. https://doi.org/10.1016/B978-0-12-815826-5.00016-7.
  • Kummerow, C., Barnes, W., Kozu, T., Shiue, J., & Simpson, J. (1998). The tropical rainfall measuring mission (TRMM) sensor package. Journal of Atmospheric and Oceanic Technology, 15(3), 809–817. https://doi.org/10.1175/1520-0426(1998)015<0809:TTRMMT>2.0.CO;2
  • Lago sochagota - especiales corpoboyacá. (2022). https://www.corpoboyaca.gov.co/sochagota/ (accessed Dec. 20, 2022).
  • Lehmann, E. L. (2006). On likelihood ratio tests. Optimality, 49, 1–8. https://doi.org/10.1214/074921706000000356
  • Lenardon, M. J., & Amirdjanova, A. (2006). Interaction between stock indices via changepoint analysis. Applied Stochastic Models in Business and Industry, 22(5–6), 573–586. https://doi.org/10.1002/asmb.653
  • Li, T., Qiao, C., Wang, L., Chen, J., & Ren, Y. (2022). An algorithm for precipitation correction in flood season based on dendritic neural network. Frontiers in Plant Science, 13, 1–13. https://doi.org/10.3389/fpls.2022.862558
  • Liu, S., Yamada, M., Collier, N., & Sugiyama, M. (2013). Change-point detection in time-series data by relative density-ratio estimation. Neural Networks: The Official Journal of the International Neural Network Society, 43, 72–83. https://doi.org/10.1016/j.neunet.2013.01.012.
  • Mas-Machuca, M., Sainz, M., & Martinez-Costa, C. (2014). A review of forecasting models for new products. Intangible Capital, 10(1), 1–25. https://doi.org/10.3926/ic.482
  • Masterton, G. (2014). What to do with a forecast? Synthese, 191(8), 1881–1907. https://doi.org/10.1007/s11229-013-0384-z
  • Mesa, L. O., Rivera, M., & Romero, J. A. (2011). Descripción general de la Inferencia Bayesiana y sus aplicaciones en los procesos de gestión. La Simulación al Serv la Acad, 2, 1–28. https://d1wqtxts1xzle7.cloudfront.net/53967330/miller_2_2-libre.pdf?1500952067=&response-content-disposition=inline%3B+filename%3DResumen_123.pdf&Expires=1712588883&Signature=BukgJhwL6JPktMjgEQK8U7FgsxaifCa3YAMlsWHP2PH19rjn4KbzpRpuxXqm8Dbkh0GDUEhS4H7YoBiMmSh2CNRarF2vm3jXU0xHJ∼UYH8qXqiem6EOVWDxFY3N5itz6Ye1DhIHEgdlC∼py8aIirIwPK-60IjcymmfKE5Gzrdumh8jbAafDjOee1sPJgeckfO2Xfaewgjxb6Wgzv-tEDqrlytG9N2OOOE305VN7uT6Tv5DVzYe∼xDOO37DN6fGlsct69YNEnRrMSAhBFpZ9-F1gk3Yjj-AeQgnMzlr5KkQls-2nPkYaJOpNaB4HEKBp-3IKN0NrDneGN9D0frIkU6A__&Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA
  • Michaelides, S., Levizzani, V., Anagnostou, E., Bauer, P., Kasparis, T., & Lane, J. E. (2009). Precipitation: Measurement, remote sensing, climatology and modeling. Atmospheric Research, 94(4), 512–533. https://doi.org/10.1016/j.atmosres.2009.08.017
  • Nastos, P. T., Paliatsos, A. G., Koukouletsos, K. V., Larissi, I. K., & Moustris, K. P. (2014). Artificial neural networks modeling for forecasting the maximum daily total precipitation at Athens, Greece. Atmospheric Research, 144, 141–150. https://doi.org/10.1016/j.atmosres.2013.11.013
  • Phan, T. T. H., Caillault, É. P., & Bigand, A. (2018). Comparative study on univariate forecasting methods for meteorological time series. The European Signal Processing Conference, 2018, 2380–2384. https://doi.org/10.23919/EUSIPCO.2018.8553576
  • Picard, D. (2013). Testing and estimating change-points in time series. Advances in Applied Probability, 17(4), 841–867. https://doi.org/10.2307/1427090
  • Plummer, P. J. (2012). Decting change–points in a compound Poisson. B.S.E.
  • Shcherbakov, M. V., Brebels, A., Shcherbakova, N. L., Tyukov, A. P., Janovsky, T. A., & Evich Kamaev, V. A. (2013). A survey of forecast error measures. World Applied Sciences Journal, 24(24), 171–176. https://doi.org/10.5829/idosi.wasj.2013.24.itmies.80032.
  • Sui, C. H., Li, X., & Yang, M. J. (2007). On the definition of precipitation efficiency. Journal of the Atmospheric Sciences, 64(12), 4506–4513. https://doi.org/10.1175/2007JAS2332.1
  • Taylor, J. W. (2011). Multi-item sales forecasting with total and split exponential smoothing. Journal of the Operational Research Society. 62(3), 555–563. https://doi.org/10.1057/jors.2010.95
  • Trenberth, K. E. (2011). Changes in precipitation with climate change. Climate Research, 47(1), 123–138. https://doi.org/10.3354/cr00953
  • Vanegas, J., & Vásquez, F. (2017). [Multivariate Adaptive Regression Splines (MARS), an alternative for the analysis of time series]. Gaceta Sanitaria, 31(3), 235–237. https://doi.org/10.1016/j.gaceta.2016.10.003.
  • Wang, T., Tian, W., & Ning, W. (2020). Likelihood ratio test change-point detection in the skew slash distribution. Communications in Statistics - Simulation and Computation, 51(9), 5068–5080. https://doi.org/10.1080/03610918.2020.1755869
  • Zhitlukhin, M. V., & Ziemba, W. T. (2016). Exit strategies in bubble-like markets using a changepoint model. Quant. Financ. Lett, 4(1), 47–52. https://doi.org/10.1080/21649502.2015.1165918
  • Zhou, Y., Fu, L., & Zhang, B. (2017). Two non parametric methods for change-point detection in distribution. Commun. Stat. - Theory Methods, 46(6), 2801–2815. https://doi.org/10.1080/03610926.2015.1048891