Does a meta-combining method lead to more accurate forecasts in the decision-making process?

• Autorzy: GULAY Emrah , ARAS Serkan
• Języki publikacji: EN

Abstrakty

EN

To improve forecasting accuracy, researchers employed various combination techniques for a long time. When researchers deal with time series data by using dissimilar models, the combined forecasts of these models are expected to be superior. Deriving a weighting scheme performing better than simple but hard−to−beat combining methods has always been challenging. In this study, a new weighting method based on the hybridisation of combining algorithms is proposed. Five popular datasets were utilised to demonstrate the effectiveness of the proposed method in an out-of-sample context. The results indicate that the proposed method leads to more accurate forecasts than other combining techniques used in the study.

Słowa kluczowe

EN

forecasting; combined forecasts; time series, meta−combining

• Wydawca: Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
• Czasopismo: Operations Research and Decisions
• Rocznik: 2024
• Tom: 34
• Numer: 3
• Strony: 101-124

Twórcy

autor

GULAY Emrah

• Department of Econometrics, Dokuz Eylul University, Turkey,

autor

ARAS Serkan

• Department of Econometrics, Dokuz Eylul University, Turkey

Bibliografia

• Adhikari, R., and Agrawal, R. K. A linear hybrid methodology for improving accuracy of time series forecasting. Neural Computing and Applications 25, 2 (2014), 269–281.
• Aiolfi, M., and Timmermann, A. Persistence in forecasting performance and conditional combination strategies. Journal of Econometrics 135, 1-2 (2006), 31–53.
• Aras, S., and Gulay, E. A new consensus between the mean and median combination methods to improve forecasting accuracy. Serbian Journal of Management 12, 2 (2017), 217–236.
• Aras, S., and Kocakoç, İ. D. A new model selection strategy in time series forecasting with artificial neural networks: IHTS. Neurocomputing 174, Part B, (2016), 974–987.
• Bacchini, F., Ciammola, A., Iannaccone, R., and Marini, M. Combining forecasts for a flash estimate of Euro area GDP. Contributi Istat No. 3, National Statistical Institute of Italy, Rome, 2010.
• Bacon, D. W., and Watts, D. G. Estimating the transition between two intersecting straight lines. Biometrika 58, 3 (1971), 525–534.
• Bates, J. M., and Granger, C. W. The combination of forecasts. Journal of the Operational Research Society 20, 4 (1969), 451–468.
• Bekiroglu, K., Duru, O., Gulay, E., Su, R., and Lagoa, C. Predictive analytics of crude oil prices by utilizing the intelligent model search engine. Applied Energy 228 (2018), 2387–2397.
• Bordignon, S., Bunn, D. W., Lisi, F., and Nan, F. Combining day-ahead forecasts for british electricity prices. Energy Economics 35 (2013), 88–103.
• Box, G. E. P., Jenkins, G. M., Reinsel, G. C., and Ljung, G. M. Time Series Analysis: Forecasting and Control. John Wiley & Sons, 2015.
• Caldeira, J. F., Moura, G. V., Nogales, F. J., and Santos, A. A. Combining multivariate volatility forecasts: an economic-based approach. Journal of Financial Econometrics 15, 2 (2017), 247–285.
• Chen, Z.-Y. Integration learning of neural network training with swarm intelligence and meta-heuristic algorithms for spot gold price forecast. Applied Artificial Intelligence 36, 1 (2022), 1994217.
• Clark, T. E., and McCracken, M. W. Combining forecasts from nested models. Oxford Bulletin of Economics and Statistics 71, 3 (2009), 303–329.
• Clemen, R. T. Combining forecasts: A review and annotated bibliography. International Journal of Forecasting 5, 4 (1989), 559–583.
• Cybenko, G. Approximation by superpositions of a sigmoidal function. Mathematics of Control, Signals and Systems 2, 4 (1989), 303–314.
• De Menezes, L. M., Bunn, D. W., and Taylor, J. W. Review of guidelines for the use of combined forecasts. European Journal of Operational Research 120, 1 (2000), 190–204.
• Dean, N. E., y Piontti, A. P., Madewell, Z. J., Cummings, D. A. T., Hitchings, M. D. T., Joshi, K., Kahn, R., Vespignani, A., Halloran, M. E., and Longini Jr, I. M. Ensemble forecast modeling for the design of Covid-19 vaccine efficacy trials. Vaccine 38, 46 (2020), 7213–7216.
• Dewangan, C. L., Singh, S. N., and Chakrabarti, S. Combining forecasts of day-ahead solar power. Energy 202 (2020), 117743.
• Di Gangi, L. Sparse convex combinations of forecasting models by meta learning. Expert Systems with Applications 200 (2022), 116938.
• Gardner Jr, E. S. Exponential smoothing: The state of the art–Part II. International Journal of Forecasting 22, 4 (2006), 637–666.
• Genre, V., Kenny, G., Meyler, A., and Timmermann, A. Combining expert forecasts: Can anything beat the simple average? International Journal of Forecasting 29, 1 (2013), 108–121.
• Graefe, A., Armstrong, J. S., Jones Jr, R. J., and Cuzán, A. G. Combining forecasts: An application to elections. International Journal of Forecasting 30, 1 (2014), 43–54.
• Gulay, E., and Duru, O. Hybrid modeling in the predictive analytics of energy systems and prices. Applied Energy 268 (2020), 114985.
• Hibon, M., and Evgeniou, T. To combine or not to combine: selecting among forecasts and their combinations. International Journal of Forecasting 21, 1 (2005), 15–24.
• Hipel, K. W., and McLeod, A. I. Time Series Modelling of Water Resources and Environmental Systems. Elsevier, 1994.
• Hong, H., Kubik, J. D., and Solomon, A. Security analysts’ career concerns and herding of earnings forecasts. The RAND Journal of Economics 31, 1 (2000), 121–144.
• Huang, H., and Lee, T.-H. To combine forecasts or to combine information? Econometric Reviews 29, 5-6 (2010), 534–570.
• Hyndman, R., Koehler, A. B., Ord, J. K., and Snyder, R. D. Forecasting with Exponential Smoothing: The State pace Approach. Springer, Berlin, 2008.
• Hyndman, R. J., Koehler, A. B., Snyder, R. D., and Grose, S. A state space framework for automatic forecasting using exponential smoothing methods. International Journal of Forecasting 18, 3 (2002), 439–454.
• Jiang, P., Liu, Z., Niu, X., and Zhang, L. A combined forecasting system based on statistical method, artificial neural networks, and deep learning methods for short-term wind speed forecasting. Energy 217 (2021), 119361.
• Jose, V. R. R., and Winkler, R. L. Simple robust averages of forecasts: Some empirical results. International Journal of Forecasting 24, 1 (2008), 163–169.
• Kourentzes, N. Intermittent demand forecasts with neural networks. International Journal of Production Economics 143, 1 (2013), 198–206.
• Lemke, C., and Gabrys, B. Meta-learning for time series forecasting and forecast combination. Neurocomputing 73, 10-12 (2010), 2006–2016.
• Lin, Y., Koprinska, I., Rana, M., and Troncoso, A. Solar power forecasting based on pattern sequence similarity and meta-learning. In Artificial Neural Networks and Machine Learning – ICANN 2020. 29th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 15–18, 2020, Proceedings, Part I (Cham, 2020), I. Farkaš, P. Masulli and S. Wermter, Eds., vol. 12396 of Lecture Notes in Computer Science, Springer, pp. 271–283.
• Luukkonen, R., Saikkonen, P., and Teräsvirta, T. Testing linearity against smooth transition autoregressive models. Biometrika 75, 3 (1988), 491–499.
• Makridakis, S., and Hibon, M. The M3-competition: results, conclusions and implications. International Journal of Forecasting 16, 4 (2000), 451–476.
• Marcjasz, G., Uniejewski, B., and Weron, R. Probabilistic electricity price forecasting with NARX networks: Combine point or probabilistic forecasts? International Journal of Forecasting 36, 2 (2020), 466–479.
• Ord, J. K., Koehler, A. B., and Snyder, R. D. Estimation and prediction for a class of dynamic nonlinear statistical models. Journal of the American Statistical Association 92, 440 (1997), 1621–1629.
• Palm, F. C., and Zellner, A. To combine or not to combine? Issues of combining forecasts. Journal of Forecasting 11, 8 (1992), 687–701.
• Priestley, M. B. Non-linear and Non-stationary Time Series Analysis. Academic Press, London, 1988.
• Rapach, D. E., Strauss, J. K., and Zhou, G. Out-of-sample equity premium prediction: Combination forecasts and links to the real economy. The Review of Financial Studies 23, 2 (2010), 821–862.
• Reid, D. J. Combining three estimates of gross domestic product. Economica 35, 140 (1968), 431–444.
• Rumelhart, D. E., Hinton, G. E., and Williams, R. J. Learning representations by back-propagating errors. Nature 323, 6088 (1986), 533–536.
• St-Aubin, P., and Agard, B. Precision and reliability of forecasts performance metrics. Forecasting 4, 4 (2022), 882–903.
• Stock, J. H., and Watson, M. W. Combination forecasts of output growth in a seven-country data set. Journal of Forecasting 23, 6 (2004), 405–430.
• Subba Rao, T., and Gabr, M. An Introduction to Bispectral Analysis and Bilinear Time Series Models. Springer Science & Business Media, 2012
• Teräsvirta, T. Specification, estimation, and evaluation of smooth transition autoregressive models. Journal of the American Statistical Association 89, 425 (1994), 208–218.
• Teräsvirta, T., and Anderson, H. M. Characterizing nonlinearities in business cycles using smooth transition autoregressive models. Journal of Applied Econometrics 7, 51 (1992), 119–136.
• Tong, H. Non-linear time series: a dynamical system approach. Oxford University Press, 1990.
• Tong, H., and Lim, K. S. Threshold autoregression, limit cycles and cyclical data. Journal of the Royal Statistical Society: Series B (Methodological) 42, 3 (1980), 245–268.
• Velleman, P. F., and Hoaglin, D. C. Applications, Basics, and Computing of Exploratory Data Analysis. Duxbury Press, 1981.
• Wallis, K. F. Combining forecasts–forty years later. In Perspectives on Econometrics and Applied Economics. M. Taylor, Ed., Routledge, 2014, pp. 39–48.
• Woodward, W. A., Gray, H. L., and Elliott, A. Stationary time series. In Applied Time Series Analysis, W. A. Woodward, H. L. Gray and A. Elliott, Eds., CRC Press, 2011, pp. 25–76.
• Yamana, T. K., Kandula, S., and Shaman, J. Individual versus superensemble forecasts of seasonal influenza outbreaks in the United States. PLoS computational biology 13, 11 (2017), e1005801.
• Yang, H., Zhu, Z., Li, C., and Li, R. A novel combined forecasting system for air pollutants concentration based on fuzzy theory and optimization of aggregation weight. Applied Soft Computing 87 (2020), 105972.
• Zhang, G., Patuwo, B. E., and Hu, M. Y. Forecasting with artificial neural networks:: The state of the art. International Journal of Forecasting 14, 1 (1998), 35–62.
• Zou, H. F., Xia, G. P., Yang, F. T., and Wang, H. Y. An investigation and comparison of artificial neural network and time series models for Chinese food grain price forecasting. Neurocomputing 70, 16-18 (2007), 2913–2923.

Identyfikatory

DOI

10.37190/ord240306