Hybrid demand forecasting models: pre-pandemic and pandemic use studies
Keywords:forecastHybrid, demand forecasting, statistic model, neural networks
Research background: In business practice and academic sphere, the question of which of the prognostic models is the most accurate is constantly present. The accuracy of models based on artificial intelligence and statistical models has long been discussed. By combining the advantages of both groups, hybrid models have emerged. These models show high accuracy. Moreover, the question remains whether data in a dynamically changing economy (for example, in a pandemic period) have changed the possibilities of using these models. The changing economy will continue to be an important element in demand forecasting in the years to come. In business, where the concept of just in time already proves to be insufficient, it is necessary to open new research questions in the field of demand forecasting.
Purpose of the article: The aim of the article is to apply hybrid models to bicycle sales e-shop data with a comparison of accuracy models in the pre-pandemic period and in the pandemic period. The paper examines the hypothesis that the pandemic period has changed the accuracy of hybrid models in comparison with statistical models and models based on artificial neural networks.
Models: In this study, hybrid models will be used, namely the Theta model and the new forecastHybrid, compared to the statistical models ETS, ARIMA, and models based on artificial neural networks. They will be applied to the data of the e-shop with the cycle assortment in the period from 1.1. 2019 to 5.10 2021. Whereas the period will be divided into two parts, pre-pandemic, i.e. until 1 March 2020 and pandemic after that date. The accuracy evaluation will be based on the RMSE, MAE, and ACF1 indicators.
Findings & value added: In this study, we have concluded that the prediction of the Hybrid model was the most accurate in both periods. The study can thus provide a scientific basis for any other dynamic changes that may occur in demand forecasting in the future. In other periods when there will be volatile demand, it is essential to choose models in which accuracy will decrease the least. Therefore, this study provides guidance for the use of methods in future periods as well. The stated results are likely to be valid even in an international comparison.
Abbasimehr, H., Shabani, M., & Yousefi, M. (2020). An optimized model using the LSTM network for demand forecasting. Computers & Industrial Engineering, 143(1), 345?366. doi:10.1016/j.cie.2020.106435.
Assimakopoulos, V., & Nikolopoulos, K. (2000). The theta model: a decomposition approach to forecasting. International Journal of Forecasting, 16(4), 521?530. doi:10.1016/S0169-2070(00)00066-2.
At?c?, R., & Pala, Z. (2022). Prediction of the Ionospheric foF2 parameter using R language forecasthybrid model library convenient time series functions. Wireless Personal Communications, 122(4), 3293?3312. doi: 10.1007/s11277-021-09050-6.
Babai, M., Dai, Y., Li, Q., Syntetos, A., & Wang, X. (2022). Forecasting of lead-time demand variance: implications for safety stock calculations. European Journal of Operational Research, 296(3), 846?861. doi: 10.1016/j.ejor.202 1.04.017.
Bahrami, M., Khashei, M., & Amindoust, A. (2021). A parallel-series hybridization of seasonal intelligent based statistical model for demand forecasting. Journal of Modelling in Management. Advance online publication. doi: 10.1108/JM2-09-2019-0235.
Balaji Prabhu, B., & Dakshayini, M. (2020). Computational performance analysis of neural network and regression models in forecasting the societal demand for agricultural food harvests. International Journal of Grid and High Performance Computing, 12(4), 35?47. doi: 10.4018/IJGHPC.2020100103.
Bates, J., & Granger, C. (2017). The combination of forecasts. Journal of the Operational Research Society, 20(4), 451?468. doi: 10.1057/jors.1969.103
Bojer, C., & Meldgaard, J. (2021). Kaggle forecasting competitions: an overlooked learning opportunity. International Journal of Forecasting, 37(2), 587?603. doi: 10.1016/j.ijforecast.2020.07.007.
Box, G., & Jenkins, G. (1976). Time series analysis, forecasting and control. San Francisco: Holden-Day.
Brown, R. G. (1959). Statistical forecasting for inventory control. New York: McGraw/Hill.
Bui, D., Le, P., Cao, M., Pham, T., & Pham, D. (2020). Accuracy improvement of various short-term load forecasting models by a novel and unified statistical data-filtering model. International Journal of Green Energy, 17(7), 382?406. doi: 10.1080/15435075.2020.1761810.
Cuhadar, M. (2020). Modelling and forecasting inbound tourism demand to Croatia using artificial neural networks: a comparative study. Journal of Tourism and Services, 11(21), 55?70. doi: 10.29036/jots.v11i21.171.
Fairlie, R., & Fossen, F. (2022). The early impacts of the COVID-19 pandemic on business sales. Small Business Economics, 58(4), 1853?1864. doi: 10.1007/s1 1187-021-00479-4.
Fildes, R., & Goodwin, P. (2021). Stability in the inefficient use of forecasting systems: a case study in a supply chain company. International Journal of Forecasting, 37(2), 1031?1046. doi: 10.1016/j.ijforecast.2020.11.004.
Foldvik Eikeland, O., Bianchi, F., Apostoleris, H., Hansen, M., Chiou, Y., & Chiesa, M. (2021). Predicting energy demand in semi-remote Arctic locations. Energies, 14(4), 798. doi: 10.3390/en14040798.
Habib, M., & Anik, M. (2022). Impacts of COVID-19 on transport modes and mobility behavior: analysis of public discourse in Twitter. Transportation Research Record: Journal of the Transportation Research Board. Advance online publication. doi: 10.1177/03611981211029926.
Holt, C. (2004). Forecasting seasonals and trends by exponentially weighted moving averages. International Journal of Forecasting, 20(1), 5?10. doi: 10.1016/j.ijforecast.2003.09.015.
Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: principles and prac-tice. Melbourne: OTexts.
Hyndman, R., & Athanasopoulos, G. (2021). Forecasting: principles and practice. Melbourne: OTexts.
Kamboj, A., Samadder, D., Rajagopal, A., & Mukhopadhyay, S. (2020). Macroeconomic statistical forecasting for engine demand. Romanian Statistical Review, 4, 63?82.
Kolková, A. (2018). Indicators of technical analysis on the basis of moving averages as prognostic models in the food industry. Journal of Competitiveness, 10(4), 102?119. doi: 10.7441/joc.2018.04.07.
Kolková, A. (2020). The application of forecasting sales of services to increase business competitiveness. Journal of Competitiveness, 12(2), 90?105. doi: 10.7441/joc.2020.02.06.
Kolková, A., & Ključnikov, A. (2021). Demand forecasting: an alternative approach based on technical indicator Pbands. Oeconomia Copernicana, 12(4), 1063?1094. doi: 10.24136/oc.2021.035.
Kolková, A., & Navrátil, M. (2021). Demand forecasting in Python: deep learning model based on LSTM architecture versus statistical models. Acta Polytechnica Hungarica, 18(8), 123?141. doi: 10.12700/APH.18.8.2021.8.7.
Kolková, A., Rozehnal, P., Gaži, F., & Fajmon, L. (2022). The use of quantitative methods in business practice: study of Czech Republic. International Journal of Entrepreneurial Knowledge, 10(1), 80?99. doi: 10.37335/ijek.v10i1.159.
Makridakis, S., Spiliotis, E., & Assimakopoulos, V. (2018). The M4 competition: results, findings, conclusion and way forward. International Journal of Forecasting, 34(4), 802?808. doi: 10.1016/j.ijforecast.2018.06.001.
Makridakis, S., Spiliotis, E., & Assimakopoulos, V. (2022). M5 accuracy competition: results, findings, and conclusions. International Journal of Forecasting. Advance line publicatin. doi: 10.1016/j.ijforecast.2021.11.013.
Marček, D. (2019). Comparison of predictive statistical learning accuracy with computational intelligence models. In IEEE 15th international scientific conference on informatics (pp. 317?322). Poprad: IEEE.
Montero-Manso, P., Athanasopoulos, G., Hyndman, R., & Talagala, T. (2020). FFORMA: feature-based forecast model averaging. International Journal of Forecasting, 36(1), 86?92. doi: 10.1016/j.ijforecast.2019.02.011.
Nontapa, C. (2021). A new hybrid forecasting using decomposition model with SARIMAX model and artificial neural network. International Journal of Mathematics and Computer Science, 16(4), 1341?1354.
Pereira, L., & Cerqueira, V. (2021). Forecasting hotel demand for revenue management using machine learning regression models. Current Issues in Tourism, 25(17), 2733?2750. doi: 10.1080/13683500.2021.1999397.
Petropoulos, F., & Makridakis, S. (2020). The M4 competition: Bigger. Stronger. Better. International Journal of Forecasting, 36(1), 3?6. doi: 10.1016/j.ijfore cast.2019.05.005.
Ramírez, J., Alarcón, J., Calzada, G., & Ponce, H. (2021). Mexican automotive industry sales behavior during the COVID-19 pandemic. Advances in Soft Computing, 265?276. doi: 10.1007/978-3-030-89820-5_22.
Shaub, D., & Ellis, P. (2020). Package ?forecastHybrid?. Retrieved from https://gitlab.com/dashaub/forecastHybrid.
Siddiqui, R., Azmat, M., Ahmed, S., & Kummer, S. (2022). A hybrid demand forecasting model for greater forecasting accuracy: the case of the pharmaceutical industry. Supply Chain Forum: An International Journal, 23(2), 124?134. doi: 10.1080/16258312.2021.1967081.
Smyl, S. (2020). A hybrid model of exponential smoothing and recurrent neural networks for time series forecasting. International Journal of Forecasting, 36(1), 75?85. doi: 10.1016/j.ijforecast.2019.03.017.
Spiliotis, E., Makridakis, S., Semenoglou, A., & Assimakopoulos, V. (2022). Comparison of statistical and machine learning models for daily SKU demand forecasting. Operational Research, 22, 3037?3061 doi: 10.1007/s12351-020-00605-2.
Tian, X., Wang, H., & Erjiang, E. (2021). Forecasting intermittent demand for inventory management by retailers: a new approach. Journal of Retailing and Consumer Services, 62. 102662. doi: 10.1016/j.jretconser.2021.102662.
Ulrich, M., Jahnke, H., Langrock, R., Pesch, R., & Senge, R. (2022). Classification-based model selection in retail demand forecasting. International Journal of Forecasting, 38(1), 209?223. doi: 10.1016/j.ijforecast.2021.05.010.
Wang, Q., Liu, C., Zhao, Y., Kitsos, A., Cannella, M., Wandg, S., & Han, L. (2020). Impacts of the COVID-19 pandemic on the dairy industry: lessons from China and the United States and policy implications. Journal of Integrative Agriculture, 19(12), 2903?2915. doi: 10.1016/S2095-3119(20)63443-8.
Winters, P. (1960). Forecasting sales by exponentially weighted Moving averages. Management Science, 6(3), 324?342. doi: 10.1287/mnsc.6.3.324.
Zhang, B., Li, N., Law, R., & Liu, H. (2021). A hybrid MIDAS approach for forecasting hotel demand using large panels of search data. Tourism Economics. Advance online publicatin. doi: 10.1177/13548166211015515.
Zhang, H., & Lu, J. (2022). Forecasting hotel room demand amid COVID-19. Tourism Economics, 28(1), 200?221. doi: 10.1177/13548166211035569
Zougagh, N., Charkaoui, A., & Echchatbi, A. (2021). Artificial intelligence hybrid models for improving forecasting accuracy. Procedia Computer Science, 184(1), 817?822. doi: 10.1016/j.procs.2021.04.013.