A Poisson-Regression, Support Vector Machine and Grey Prediction Based Combined Forecasting Model Proposal: A Case Study in Distribution Business

Year 2021, , 23 - 35, 31.12.2021

Fatih Yiğit , Şakir Esnaf , Bahar Yalçın Kavuş

https://doi.org/10.34110/forecasting.957494

Abstract

Demand forecasting is a complicated task due to incomplete data and unpredictability. Accurate demand forecasting has a direct impact on the performance of a company. The goal of the study is to present a new two-stage combination model named Hybrid-2-Best, for accurate demand forecasting. The model combines three forecasting models in a single combined forecast. The Hybrid-2-Best model uses a two-stage algorithm to achieve better-performing forecasts. Case study showed that the proposed Hybrid-2-Best model performs the best forecast performance among other combination techniques and individual methods. Furthermore, GP integration in the first and second stages gives flexibility. Experimental results indicate that the proposed Hybrid-2-Best model is a promising alternative for sales demand forecasting. MAPE of the proposed model is 0,13. This is a good result and better than compared other models. Proposed model performed better than other compared models in MASE and MSE as well

Keywords

grey prediction, poisson-regression, support vector machine, combined forecasting, sales demand forecasting

References

• [1] J.S. Armstrong, Combining forecasts: The end of the beginning or the beginning of the end?, Int. J. Forecast. 5 (1989) 585–588. doi:10.1016/0169-2070(89)90013-7.
• [2] L. Si-Feng, Emergence and Development of Grey System Theory and Its Forward Trends [J], J. Nanjing Univ. Aeronaut. \& Astronaut. 2 (2004) 266–272.
• [3] E.S. Gardner, E. Mckenzie, Forecasting Trends in Time Series, Manage. Sci. 31 (1985) 1237–1246. doi:10.1287/mnsc.31.10.1237.
• [4] A. Aslanargun, M. Mammadov, B. Yazici, S. Yolacan, Comparison of ARIMA, neural networks and hybrid models in time series: Tourist arrival forecasting, J. Stat. Comput. Simul. 77 (2007) 29–53. doi:10.1080/10629360600564874.
• [5] W.K. Wong, Z.X. Guo, A hybrid intelligent model for medium-term sales forecasting in fashion retail supply chains using extreme learning machine and harmony search algorithm, Int. J. Prod. Econ. 128 (2010) 614–624. doi:10.1016/j.ijpe.2010.07.008.
• [6] Y. Zhang, L. Luo, J. Yang, D. Liu, R. Kong, Y. Feng, A hybrid ARIMA-SVR approach for forecasting emergency patient flow, J. Ambient Intell. Humaniz. Comput. 10 (2019) 3315–3323. doi:10.1007/s12652-018-1059-x.
• [7] P. Lalou, S.T. Ponis, O.K. Efthymiou, Demand Forecasting of Retail Sales Using Data Analytics and Statistical Programming, Manag. Mark. 15 (2020) 186–202. doi:10.2478/mmcks-2020-0012.
• [8] F. Başoğlu Kabran, K. Demirberk Ünlü, A two-step machine learning approach to predict S&P 500 bubbles, J. Appl. Stat. (2020). doi:10.1080/02664763.2020.1823947.
• [9] M. Wahiduzzaman, A. Yeasmin, Statistical forecasting of tropical cyclone landfall activities over the North Indian Ocean rim countries, Atmos. Res. 227 (2019) 89–100. doi:10.1016/j.atmosres.2019.04.034.
• [10] C.A. Graff, S.R. Coffield, Y. Chen, E. Foufoula-Georgiou, E. Foufoula-Georgiou, J.T. Randerson, P. Smyth, Forecasting daily wildfire activity using poisson regression, IEEE Trans. Geosci. Remote Sens. 58 (2020) 4837–4851. doi:10.1109/TGRS.2020.2968029.
• [11] T. KIm, B. Lieberman, G. Luta, E. Pena, Prediction Regions for Poisson and Over-Dispersed Poisson Regression Models with Applications to Forecasting Number of Deaths during the COVID-19 Pandemic, (2020). http://arxiv.org/abs/2007.02105.
• [12] M. Pan, C. Li, R. Gao, Y. Huang, H. You, T. Gu, F. Qin, Photovoltaic power forecasting based on a support vector machine with improved ant colony optimization, J. Clean. Prod. 277 (2020). doi:10.1016/j.jclepro.2020.123948.
• [13] M.T. Bilişik, F.H. Sezgin, Ş. Esnaf, Dynamic Pricing for Revenue Management in Retailing Using Support Vector Machine , Poisson Regression and Nonlinear Programming, Eurasian Bus. Econ. J. 8 (2017) 11–34.
• [14] K.S. Sahoo, B.K. Tripathy, K. Naik, S. Ramasubbareddy, B. Balusamy, M. Khari, D. Burgos, An Evolutionary SVM Model for DDOS Attack Detection in Software Defined Networks, IEEE Access. 8 (2020) 132502–132513. doi:10.1109/ACCESS.2020.3009733.
• [15] D. Ju-Long, Control problems of grey systems, Syst. Control Lett. 1 (1982) 288–294.
• [16] D. Camelia, Grey systems theory in economics--bibliometric analysis and applications’ overview, Grey Syst. Theory Appl. (2015).
• [17] E. Kose, D. Vural, G. Canbulut, The most livable city selection in Turkey with the grey relational analysis, Grey Syst. Theory Appl. (2020).
• [18] L. Suganthi, A.A. Samuel, Energy models for demand forecasting - A review, Renew. Sustain. Energy Rev. 16 (2012) 1223–1240. doi:10.1016/j.rser.2011.08.014.
• [19] F. Gao, Application of improved grey theory prediction model in medium-term load forecasting of distribution network, in: Proc. - 2019 7th Int. Conf. Adv. Cloud Big Data, CBD 2019, Institute of Electrical and Electronics Engineers Inc., 2019: pp. 151–155. doi:10.1109/CBD.2019.00036.
• [20] Y.-C. Chai, Y.-F. Huang, H.-S. Dang, An application of grey prediction to estimate the market size of tutoring industry in Taiwan, in: 2017 Int. Conf. Appl. Syst. Innov., IEEE, 2017: pp. 1740–1743.
• [21] M. Negnevitsky, P.L. Johnson, S. Santoso, Short term wind power forecasting using hybrid intelligent systems, (2007).
• [22] T. Hui, N. Dongxiao, Combining simulate anneal algorithm with support vector regression to forecast wind speed, in: 2010 Second IITA Int. Conf. Geosci. Remote Sens., IEEE, 2010: pp. 92–94.
• [23] S.P. Wang, A.M. Hu, Z.X. Wu, Y.Q. Liu, X.W. Bai, Multiscale combined model based on run-length-judgment method and its application in oil price forecasting, Math. Probl. Eng. 2014 (2014). doi:10.1155/2014/513201.
• [24] P. Qin, C. Cheng, Prediction of Seawall Settlement Based on a Combined LS-ARIMA Model, Math. Probl. Eng. 2017 (2017). doi:10.1155/2017/7840569.
• [25] S. Karthika, V. Margaret, K. Balaraman, Hybrid short term load forecasting using ARIMA-SVM, in: 2017 Innov. Power Adv. Comput. Technol., IEEE, 2017: pp. 1–7.
• [26] Z. Liu, P. Jiang, L. Zhang, X. Niu, A combined forecasting model for time series: Application to short-term wind speed forecasting, Appl. Energy. 259 (2020) 114137. doi:10.1016/j.apenergy.2019.114137.
• [27] M.E. Thomson, A.C. Pollock, D. Önkal, M.S. Gönül, Combining forecasts: Performance and coherence, Int. J. Forecast. 35 (2019) 474–484. doi:10.1016/j.ijforecast.2018.10.006.
• [28] F.X. Diebold, M. Shin, Machine learning for regularized survey forecast combination: Partially-egalitarian LASSO and its derivatives, Int. J. Forecast. 35 (2019) 1679–1691. doi:10.1016/j.ijforecast.2018.09.006.
• [29] N. Kourentzes, D. Barrow, F. Petropoulos, Another look at forecast selection and combination: Evidence from forecast pooling, Int. J. Prod. Econ. 209 (2019) 226–235. doi:10.1016/j.ijpe.2018.05.019.
• [30] A. Al Mamun, M. Sohel, N. Mohammad, M.S. Haque Sunny, D.R. Dipta, E. Hossain, A Comprehensive Review of the Load Forecasting Techniques Using Single and Hybrid Predictive Models, IEEE Access. 8 (2020) 134911–134939. doi:10.1109/access.2020.3010702.
• [31] Vapnik, Vapnik, V. (2013). The nature of statistical learning theory. Springer science & business media., (1995).
• [32] Y. Zhang, M. Li, Grey system forecasting based on MATLAB and its example application, in: 2010 2nd IEEE Int. Conf. Inf. Manag. Eng., IEEE, 2010: pp. 48–52.
• [33] D. Julong, Introduction to grey system theory, J. Grey Syst. 1 (1989) 1–24.
• [34] H.M. Hsu, C.T. Chen, Aggregation of fuzzy opinions under group decision making, Fuzzy Sets Syst. 79 (1996) 279–285. doi:10.1016/0165-0114(95)00185-9.
• [35] L. Ling, D. Zhang, A.W. Mugera, S. Chen, Q. Xia, A Forecast Combination Framework with Multi-Time Scale for Livestock Products’ Price Forecasting, Math. Probl. Eng. 2019 (2019). doi:10.1155/2019/8096206.
• [36] C.D. Lewis, Industrial and business forecasting methods: A practical guide to exponential smoothing and curve fitting, Butterworth-Heinemann, 1982.