La producción de conocimiento científico sobre la pronóstica de series tiempo jerarquicas: un enfoque bibliométrico
DOI:
https://doi.org/10.47456/bjpe.v10i1.43222Palabras clave:
Serie temporal jerárquica, Modelos Predictivos, bibliometriaResumen
Los estudios sobre series de tiempo jerárquicas han llamado la atención en la literatura. En general, una serie temporal jerárquica consiste en un conjunto de información recopilada a lo largo del tiempo, que se organiza mediante agrupaciones como ubicación geográfica, tipo de producto y otros atributos, por ejemplo. Este artículo realizó, de manera pionera, una investigación bibliométrica sobre todas las publicaciones indexadas en la base de datos Web of Science, en series temporales jerárquicas, durante el período de 1996 a 2020. Es de destacar que el crecimiento anual de las publicaciones sobre este tema es equivalente al 13,45% y que Estados Unidos de América se destaca como el mayor polo de producción de conocimiento sobre series jerárquicas, concentrando aproximadamente el 30% de la investigación. Finalmente, se encontró que los métodos híbridos para la conciliación óptima de pronósticos, basados en algoritmos de aprendizaje automático, han sido recurrentes en las investigaciones actuales.
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Alkema, L., Chao, F., You, D., Pedersen, J., & Sawyer, C. C. (2014). National, regional, and global sex ratios of infant, child, and under-5 mortality and identification of countries with outlying ratios: a systematic assessment. The Lancet Global Health, 2(9), e521-e530. https://doi.org/10.1016/S2214-109X(14)70280-3 DOI: https://doi.org/10.1016/S2214-109X(14)70280-3
Aria, M. & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of informetrics, 11(4), 959-975. https://doi.org/10.1016/j.joi.2017.08.007 DOI: https://doi.org/10.1016/j.joi.2017.08.007
Athanasopoulos, G., Hyndman, R. J., Kourentzes, N., & Petropoulos, F. (2017). Forecasting with temporal hierarchies. European Journal of Operational Research, 262(1), 60-74. https://doi.org/10.1016/j.ejor.2017.02.046 DOI: https://doi.org/10.1016/j.ejor.2017.02.046
Athanasopoulos, G., Gamakumara, P., Panagiotelis, A., Hyndman, R. J., & Affan, M. (2020). Hierarchical forecasting. In Macroeconomic Forecasting in the Era of Big Data (pp. 689-719). Springer, Cham. https://doi.org/10.1007/978-3-030-31150-6_21 DOI: https://doi.org/10.1007/978-3-030-31150-6_21
Bearak, J., Popinchalk, A., Alkema, L., & Sedgh, G. (2018). Global, regional, and subregional trends in unintended pregnancy and its outcomes from 1990 to 2014: estimates from a Bayesian hierarchical model. The Lancet Global Health, 6(4), e380-e389. https://doi.org/10.1016/S2214-109X(18)30029-9 DOI: https://doi.org/10.1016/S2214-109X(18)30029-9
Berliner, L. M. (1996). Hierarchical Bayesian time series models. In Maximum entropy and Bayesian methods (pp. 15-22). Springer, Dordrecht. https://doi.org/10.1007/978-94-011-5430-7_3 DOI: https://doi.org/10.1007/978-94-011-5430-7_3
Bojer, C. & Meldgaard, J. P. (2020). The M5: A Preview from Prior Competitions. Foresight: The International Journal of Applied Forecasting, (58), 17-23.
Freitas, T., de. (2016). Modelos e aplicações para séries temporais hierárquicas: abordagens de reconciliação ótima e proporções de previsão. 91 p. Dissertação (Mestrado em Engenharia de Produção) - Pontifícia Universidade Católica do Rio de Janeiro, Rio de Janeiro.
Greeff, S. C., de, Dekkers, A. L., Teunis, P., Rahamat-Langendoen, J. C., Mooi, F. R., & Melker, H. E., de. (2009). Seasonal patterns in time series of pertussis. Epidemiology & Infection, 137(10), 1388-1395. https://doi.org/10.1017/S0950268809002489 DOI: https://doi.org/10.1017/S0950268809002489
Faloutsos, C., Gasthaus, J., Januschowski, T., & Wang, Y. (2019, June). Classical and contemporary approaches to big time series forecasting. In Proceedings of the 2019 International Conference on Management of Data (pp. 2042-2047). https://doi.org/10.1145/3299869.3314033 DOI: https://doi.org/10.1145/3299869.3314033
Fliedner, G. (1999). An investigation of aggregate variable time series forecast strategies with specific subaggregate time series statistical correlation. Computers & operations research, 26(10-11), 1133-1149. https://doi.org/10.1016/S0305-0548(99)00017-9 DOI: https://doi.org/10.1016/S0305-0548(99)00017-9
Fliedner, G. (2001). Hierarchical forecasting: issues and use guidelines. Industrial Management & Data Systems. https://doi.org/10.1108/02635570110365952 DOI: https://doi.org/10.1108/02635570110365952
Hyndman, R. J., Ahmed, R. A., Athanasopoulos, G., & Shang, H. L. (2011). Optimal combination forecasts for hierarchical time series. Computational statistics & data analysis, 55(9), 2579-2589. https://doi.org/10.1016/j.csda.2011.03.006 DOI: https://doi.org/10.1016/j.csda.2011.03.006
Hyndman, R. J. & Athanasopoulos, G. (2018). Forecasting: principles and practice. OTexts.
Karmy, J. P. & Maldonado, S. (2019). Hierarchical time series forecasting via support vector regression in the European travel retail industry. Expert Systems with Applications, 137, 59-73. https://doi.org/10.1016/j.eswa.2019.06.060 DOI: https://doi.org/10.1016/j.eswa.2019.06.060
Lauretto, M., Nakano, F., Pereira, C. A. B., & Stern, J. M. (2008, November). Hierarchical Forecasting with Functional Trees. In AIP Conference Proceedings (Vol. 1073, No. 1, pp. 317-324). American Institute of Physics. https://doi.org/10.1063/1.3039015 DOI: https://doi.org/10.1063/1.3039015
Lauretto, M. S., Nakano, F., Pereira, C. A. B., & Stern, J. M. (2009). Hierarchical forecasting with polynomial nets. In New Advances in Intelligent Decision Technologies (pp. 305-315). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00909-9_30 DOI: https://doi.org/10.1007/978-3-642-00909-9_30
Ma, S., Fildes, R., & Huang, T. (2016). Demand forecasting with high dimensional data: The case of SKU retail sales forecasting with intra-and inter-category promotional information. European Journal of Operational Research, 249(1), 245-257. https://doi.org/10.1016/j.ejor.2015.08.029 DOI: https://doi.org/10.1016/j.ejor.2015.08.029
Makridakis, S., Spiliotis, E., & Assimakopoulos, V. (2020). The M4 Competition: 100,000 time series and 61 forecasting methods. International Journal of Forecasting, 36(1), 54-74. https://doi.org/10.1016/j.ijforecast.2019.04.014 DOI: https://doi.org/10.1016/j.ijforecast.2019.04.014
Nielsen, H. A. & Madsen, H. (2001). A generalization of some classical time series tools. Computational Statistics & Data Analysis, 37(1), 13-31. https://doi.org/10.1016/S0167-9473(00)00061-X DOI: https://doi.org/10.1016/S0167-9473(00)00061-X
Pinheiro, S. M. (2015). Previsão Hierárquica Aplicada às Políticas Públicas de Transporte Rodoviário. Monografia. 61 p. Trabalho de Conclusão de Curso (Especialização em Estatística) - Universidade Federal de Minas Gerais, Brasil.
Sedgh, G., Bearak, J., Singh, S., Bankole, A., Popinchalk, A., Ganatra, B., ... & Alkema, L. (2016). Abortion incidence between 1990 and 2014: global, regional, and subregional levels and trends. The Lancet, 388(10041), 258-267. https://doi.org/10.1016/S0140-6736(16)30380-4 DOI: https://doi.org/10.1016/S0140-6736(16)30380-4
Silveira G., T. & Azevedo C., M. (2020). Forecasting hierarchical time series in power generation. Energies, 13(14), 3722. https://doi.org/10.3390/en13143722 DOI: https://doi.org/10.3390/en13143722
Smyl, S. (2020). A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting. International Journal of Forecasting, 36(1), 75-85. https://doi.org/10.1016/j.ijforecast.2019.03.017 DOI: https://doi.org/10.1016/j.ijforecast.2019.03.017
Sun, Y., Zhang, X., & Wang, S. (2020). A hierarchical forecasting model for China’s foreign trade. Journal of Systems Science and Complexity, 33(3), 743-759. https://doi.org/10.1007/s11424-020-8070-y DOI: https://doi.org/10.1007/s11424-020-8070-y
Van Eck, N. J. & Waltman, L. (2014). Visualizing bibliometric networks. In Measuring scholarly impact (pp. 285-320). Springer, Cham. https://doi.org/10.1007/978-3-319-10377-8_13 DOI: https://doi.org/10.1007/978-3-319-10377-8_13
Veríssimo, F. V. (2016). Modelo de Séries Temporais Hierárquicas de Previsão de Vendas Aplicado à Indústria do Calçado. 79 p. Dissertação (Mestrado em Modelação, Análise de Dados e Sistemas de Apoio à Decisão) - Faculdade de Economia do Porto, Portugal
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