Posterior Variance Test: Ex ante Evaluation of Grey Forecasting model
DOI:
https://doi.org/10.52812/ijgs.71Keywords:
Posterior Variance Test, Grey Forecast, Forecast Error, Forecast Accuracy Measurement, Methane EmissionsAbstract
Scholars frequently use ex post evaluation metrics such as the Mean Absolute Percentage Error (MAPE) to estimate the forecast accuracy. However, ex ante metrics are essential to know whether a given forecasting model is suitable for a given variable irrespective of the outcome of ex post evaluations. The ex ante measures help us ensure that the forecast is accurate, not by chance. The current study presents the Posterior Variance Test (PVT), which can serve as an ex ante measure of grey forecast accuracy. The study forecasted the methane emissions from Australia and India using a grey forecasting model and found that even though the MAPE generated "accurate forecasts" for both cases, the PVT invalidated the model's suitability for one of the two cases. The data visualization also corroborated the outcome of the PVT.
References
Ahmed, R., Sreeram, V., Mishra, Y., & Arif, M. D. (2020). A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization. Renewable and Sustainable Energy Reviews, 124, 109792. https://doi.org/10.1016/j.rser.2020.109792
Al-Rasheed, K. A., & Soliman, E. (2022). Difference in S-curve for different types of construction projects. Journal of Engineering Research, 10(1B), 17-28. https://doi.org/10.36909/jer.10231
Ayvaz, B., & Kusakci, A. O. (2017). Electricity consumption forecasting for Turkey with non-homogeneous discrete grey model. Energy Sources Part B – Economics Planning and Policy, 12(3), 260–267. https://doi.org/10.1080/15567249.2015.1089337
Bass, B., Akkur, N., Russo, J., & Zack, J. (1996). Modelling the Biospheric Aspects of the Hydrological Cycle – Upscaling Processes and Downscaling Weather Data. In: Regional Hydrological Respon.se to Climate Change (J. A. A. Jones et al. (eds.)), 39-62. https://doi.org/10.1007/978-94-011-5676-9_3
Bin, H. (2014). Using grey system theory, the port traffic volume and market share are forecasted and analyzed [运用灰色系统理论开展港口运量及占有率预测分析], Business Culture, 20, 213-214.
Boamah, V. (2021). Forecasting the Demand of Oil in Ghana: A Statistical Approach. Management Science and Business Decisions, 1(1), 29-43. https://doi.org/10.52812/msbd.25
Boardman, A. E., Mallery, W. L., & Vining, A. R. (1994). Learning from ex ante/ex post cost-benefit comparisons: the Coquihalla highway example. Socio-Economic Planning Sciences, 28(2), 69-84.
Chen, C.-I., & Huang, S.-J. (2013). The necessary and sufficient condition for GM(1, 1) grey prediction model. Applied Mathematics and Computation, 219(11), 6152-6162. https://doi.org/10.1016/j.amc.2012.12.015
Clements, M. P. (2014). Forecast uncertainty – ex ante and ex post: US inflation and output growth. Journal of Business & Economic Statistics, 32(2), 206-216.
Collins. (2023a). Ex ante. Collins English Dictionary. https://www.collinsdictionary.com/dictionary/english/ex-ante
Collins. (2023b). Ex post. Collins English Dictionary. https://www.collinsdictionary.com/dictionary/english/ex-post
Cudjoe, D., Brahim, T., & Zhu, B. (2023). Assessing the economic and ecological viability of generating electricity from oil derived from pyrolysis of plastic waste in China. Waste Management, 168, 354-365. https://doi.org/10.1016/j.wasman.2023.06.015
Deng, J. (1984). The Differential Grey Model (GM) and its Implement in Long Period Forecasting of Grain [灰色动态模型(GM)及在粮食长期预测中的应用], Exploration of Nature Magazine, 3, 37-43.
Deng, J. (1996). Basic Methods of Grey Systems [灰色系统基本方法](4th Ed.). Huazhong University of Science and Technology Press. ISBN: 7-5609-0045-3
EPA. (2023). Importance of Methane. The United States Environmental Protection Agency. https://www.epa.gov/gmi/importance-methane
Gowrisankar, A., Priyanka, T. M. C., Saha, A., Rondoni, L., Kamrul Hassan, M., & Banerjee, S. (2022). Greenhouse gas emissions: A rapid submerge of the world. Chaos: An Interdisciplinary Journal of Nonlinear Science, 32(6), 061104. https://doi.org/10.1063/5.0091843
Guo, R., & Guo, D. (2009). Random fuzzy variable foundation for grey differential equation modeling. Soft Computing, 13, 185-201. https://doi.org/10.1007/s00500-008-0301-4
Javed S. A, Zhu, B., & Liu S. (2020). Forecast of Biofuel Production and Consumption in Top CO2 Emitting Countries using a novel grey model. Journal of Cleaner Production, 276, 123977. https://doi.org/10.1016/j.jclepro.2020.123997
Javed, S. A., & Cudjoe, D. (2022). A novel Grey Forecasting of Greenhouse Gas Emissions from four Industries of China and India. Sustainable Production and Consumption, 29, 777-790. https://doi.org/10.1016/j.spc.2021.11.017
Javed, S. A., Gunasekaran, A., & Mahmoudi, A. (2022). DGRA: Multi-sourcing and Supplier Classification through Dynamic Grey Relational Analysis method. Computers & Industrial Engineering, 173, 108674. https://doi.org/10.1016/j.cie.2022.108674
Khan, A. M., & Osińska, M. (2023). Comparing forecasting accuracy of selected grey and time series models based on energy consumption in Brazil and India. Expert Systems with Applications, 212, 118840. https://doi.org/10.1016/j.eswa.2022.118840
Kırbaş, İ., Sözen, A., Tuncer, A. D., & Kazancıoğlu, F. Ş. (2020). Comparative analysis and forecasting of COVID-19 cases in various European countries with ARIMA, NARNN and LSTM approaches. Chaos, Solitons & Fractals, 138, 110015. https://doi.org/10.1016/j.chaos.2020.110015
Klimberg, R. K., Sillup, G. P., Boyle, K. J., & Tavva, V. (2015). Forecasting performance measures – what are their practical meaning?. In: Advances in Business and Management Forecasting. http://dx.doi.org/10.1108/S1477-4070(2010)0000007012
Lin, Y., Chen, M. Y., & Liu, S. (2004). Theory of grey systems: capturing uncertainties of grey information. Kybernetes, 33(2), 196-218. https://doi.org/10.1108/03684920410514139
Liu, A., Lin, V. S., Li, G., & Song, H. (2022). Ex ante tourism forecasting assessment. Journal of Travel Research, 61(1), 64-75. https://doi.org/10.1177/0047287520974456
Liu, S., Yang, Y, Wu, L., et al. (2014). Grey system theory and its application [灰色系统理论及其应用] (7th Ed.). Beijing: Science Press.
Liu, S., Yang, Y., & Forrest, J. Y.-L. (2022). Grey Systems Analysis – Methods, Models and Applications. Singapore: Springer.
Loh, T. P., Cooke, B. R., Markus, C., Zakaria, R., Tran, M. T. C., Ho, C. S., ... & IFCC Working Group on Method Evaluation Protocols. (2023). Method evaluation in the clinical laboratory. Clinical Chemistry and Laboratory Medicine (CCLM), 61(5), 751-758. https://doi.org/10.1515/cclm-2022-0878
Ma, X., Wu, W., Zeng, B., Wang, Y., & Wu, X. (2020). The conformable fractional grey system model. ISA Transactions, 96, 255-271. https://doi.org/10.1016/j.isatra.2019.07.009
Moody, D. L. (2003). The method evaluation model: a theoretical model for validating information systems design methods. ECIS 2003 Proceedings, 79. http://aisel.aisnet.org/ecis2003/79
Podrecca, M., & Sartor, M. (2023). Forecasting the diffusion of ISO/IEC 27001: a Grey model approach. The TQM Journal, 35(9), 123-151. https://doi.org/10.1108/TQM-07-2022-0220
Rastrigin, L. (1984). This Chancy, Chancy, Chancy World. Moscow: Mir Publishers.
Savić, D. (2019). When is 'grey' too 'grey'? – A case of grey data. The Grey Journal, 15(2), 71-76.
Singh, P. K., Pandey, A. K., & Bose, S. C. (2022). A new grey system approach to forecast closing price of Bitcoin, Bionic, Cardano, Dogecoin, Ethereum, XRP Cryptocurrencies. Quality & Quantity, 57, 2429–2446. https://doi.org/10.1007/s11135-022-01463-0
Steece, B. (1986). Ex-ante measures for evaluating forecasting models. Engineering Costs and Production Economics, 10(1), 25-34. https://doi.org/10.1016/0167-188X(86)90017-0
Steiger, O. (2018). Ex ante and Ex post. In: The New Palgrave Dictionary of Economics. London: Palgrave Macmillan. https://doi.org/10.1057/978-1-349-95189-5_315
Talafuse, T. P., & Pohl, E. A. (2017). Small sample reliability growth modeling using a grey systems model. Quality Engineering, 29(3), 455-467. https://doi.org/10.1080/08982112.2017.1318920
Tan, X., Deng, J., & Chen, X. (2007). Generalized grey relational grade and grey relational order test. In: 2007 IEEE International Conference on Systems, Man and Cybernetics (pp. 3928-3931). IEEE. https://doi.org/10.1109/ICSMC.2007.4414141
Tian, X., Wu, W., Ma, X., & Zhang, P. (2021). A new information priority accumulated grey model with hyperbolic sinusoidal term and its applications. International Journal of Grey Systems, 1(2), 5-19. https://doi.org/10.52812/ijgs.27
Tulkinov, S. (2023). Grey forecast of electricity production from coal and renewable sources in the USA, Japan and China. Grey Systems: Theory and Application, 13(3), 517-543. https://doi.org/10.1108/GS-10-2022-0107
UNEP. (2021). Methane emissions are driving climate change. Here's how to reduce them. The United Nations Environment Programme. https://www.unep.org/news-and-stories/story/methane-emissions-are-driving-climate-change-heres-how-reduce-them
Wei, B., & Xie, N. (2022). On unified framework for continuous-time grey models: An integral matching perspective. Applied Mathematical Modelling, 101, 432-452. https://doi.org/10.1016/j.apm.2021.09.008
Wu, W., Ma, X., Zhang, H., Tian, X., Zhang, G., & Zhang, P. (2022). A Conformable Fractional Discrete Grey Model CFDGM (1,1) and its Application. International Journal of Grey Systems, 2(1), 5-15. https://doi.org/10.52812/ijgs.36
Xie, N. (2022). A summary of grey forecasting models. Grey Systems: Theory and Application, 12(4), 703-722. https://doi.org/10.1108/GS-06-2022-0066
Yu, W., Xia, L., & Cao, Q. (2023). Forecasting digital economy of China using an Adaptive Lasso and grey model optimized by particle swarm optimization algorithm. Journal of Intelligent & Fuzzy Systems, 44(2), 2543-2560. https://doi.org/10.3233/jifs-222520
.

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Natural Science Research of Jiangsu Higher Education Institutions of China
Grant numbers 21KJB480011