AIRCC's International Journal of Computer Science and Information Technology

Open Access Open Access  Restricted Access Subscription Access

Application of Facebook's Prophet Algorithm for Successful Sales Forecasting Based on Real-World Data


Affiliations
1 Info Studio d.o.o. Sarajevo, Bosnia and Herzegovina
2 University of Sarajevo, Bosnia and Herzegovina
 

This paper presents a framework capable of accurately forecasting future sales in the retail industry and classifying the product portfolio according to the expected level of forecasting reliability. The proposed framework, that would be of great use for any company operating in the retail industry, is based on Facebook's Prophet algorithm and backtesting strategy. Real-world sales forecasting benchmark data obtained experimentally in a production environment in one of the biggest retail companies in Bosnia and Herzegovina is used to evaluate the framework and demonstrate its capabilities in a real-world use case scenario.

Keywords

Sales Forecasting, Real-World Dataset, Prophet, Backtesting, Classification.
User
Notifications
Font Size

  • Aras, S., Deveci Kocakoç, İ. and Polat, C. (2017). Comparative study on retail sales forecasting between single and combination methods. J. Bus. Econ. Manag. https://doi.org/10.3846/16111699.2017.1367324

  • Ansuj, A. P., Camargo, M. E., Radharamanan, R., and Petry, D. G. (1996). Sales forecasting using time series and neural networks. Computers & Industrial Engineering 31(1): 421–424. https://doi.org/10.1016/0360-8352(96)00166-0

  • Alon, I., Qi, M., and Sadowski, R. J. (2001). Forecasting aggregate retail sales: a comparison of artificial neural networks and traditional methods. Journal of Retailing and Consumer Services 8(3): 147–156. https://doi.org/10.1016/S0969-6989(00)00011-4

  • Frank, C., Garg, A., Sztandera, L., and Raheja, A. (2003). Forecasting women’s apparel sales using mathematical modelling. International Journal of Clothing Science and Technology 15(2): 107–125. https://doi.org/10.1108/09556220310470097

  • Aburto, L., and Weber, R. (2007). Improved supply chain man-agement based on hybrid demand forecasts. Apl. Soft Computing 7(1): 136–144. https://doi.org/10.1016/j.asoc.2005.06.001

  • Au, K. F., Choi, T. M., and Yu, Y. (2008). Fashion retail forecasting by evolutionary neural networks. International Journal of Pro-duction Economics 114(2): 615–630. https://doi.org/10.1016/j.ijpe.2007.06.013

  • Pan, Y., Pohlen, T., and Manago, S. (2013). Hybrid neural network model in forecasting aggregate US retail sales. Advances in Business and Management Forecasting 9: 153–170. https://doi.org/10.1108/S1477-4070(2013)0000009013

  • Dwivedi, A., Niranjan, M., and Sahu, K. (2013). A business intelli-gence technique for forecasting the automobile sales using Adaptive Intelligent Systems (ANFIS and ANN). International Journal of Computer Applications 74(9): 7–13. https://doi.org/10.5120/12911-9383

  • Aye, G. C., Balcilar, M., Gupta, R., and Majumdar, A. (2015). Forecasting aggregate retail sales: the case of South Africa. International Journal of Production Economics 160: 66–79. https://doi.org/10.1016/j.ijpe.2014.09.033

  • Ramos, P., Santos, N., and Rebelo, R. (2015). Performance of state space and ARIMA models for consumer retail sales forecasting. Robotics and Computer-Integrated Manufacturing 34: 151–163. https://doi.org/10.1016/j.rcim.2014.12.015

  • Fabianová, J., Kačmáry, P., Molnár, V., and Michalik, P. (2016). Using a software tool in forecasting: a case study of sales forecasting taking into account data uncertainty. Open Engineering 6(1): 270–279. https://doi.org/10.1515/eng-2016-0033

  • Kolassa, S. (2016). Evaluating predictive count data distributions in retail sales forecasting. International Journal of Forecasting 32(3): 788–803. https://doi.org/10.1016/j.ijforecast.2015.12.004

  • Ma, S., Fildes, R., and 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

  • Jiménez, F., Sánchez, G., García, J. M., Sciavicco, G., and Miralles, L. (2017). Multi-objective evolutionary feature selection for online sales forecasting. Neurocomputing 234: 75–92. https://doi.org/10.1016/j.neucom.2016.12.045

  • Ramos, P., and Fildes, R. (2018). An evaluation of retail forecasting methods for promotions. In. Lancaster: Dept. Management Science.

  • Papacharalampous, G. A., and Tyralis, H. (2018). Evaluation of random forests and Prophet for daily streamflow forecasting. Advances in Geoscience 45: 201-208. https://doi:10.5194/adgeo-45-201-2018

  • Zunic, E., Hodzic, K., Hasic, H., Skrobo, R., Besirevic, A., and Donko, D., (2017). Application of advanced analysis and predictive algorithm for warehouse picking zone capacity and content prediction. in ICAT 2017 - 26th International Conference on Information, Communication and Automation Technologies, Proceedings. 1-6. https://doi.org/10.1109/ICAT.2017.8171629

  • Zunic, E., Besirevic, A., Delalic, S., Hodzic, K., and Hasic, H. (2018). A generic approach for order picking optimization process in different warehouse layouts. in 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics, MIPRO 2018 - Proceedings. 1000-1005. https://doi.org/10.23919/MIPRO.2018.8400183

  • Zunic, E., Delalic, S., Hodzic, K., Besirevic, A., and Hindija, H. (2018). Smart Warehouse Management System Concept with Implementation. in 2018 14th Symposium on Neural Networks and Applications, NEUREL 2018. 1-5. https://doi.org/10.1109/NEUREL.2018.8587004

  • Zunic, E., Hindija, H., Besirevic, A., Hodzic, K., and Delalic, S. (2018). Improving Performance of Vehicle Routing Algorithms using GPS Data. in 2018 14th Symposium on Neural Networks and Applications, NEUREL 2018. https://doi.org/10.1109/NEUREL.2018.8586982

  • Zunic, E., and Đonko, D. (2019). Parameter setting problem in the case of practical vehicle routing problems with realistic constraints. in Proceedings of the 2019 Federated Conference on Computer Science and Information Systems, FedCSIS 2019. https://doi.org/10.15439/2019F194

  • Žunić, E., Đonko, D., and Buza, E. (2020). An Adaptive Data-Driven Approach to Solve Real-World Vehicle Routing Problems in Logistics. Complexity. https://doi.org/10.1155/2020/7386701

  • Taylor, S. J., and Letham, B. (2018). Forecasting at scale. The American Statistician, 72(1), 37-45. https://doi.org/10.1080/00031305.2017.1380080

  • Žunić, E. (2019). Real-world sales forecasting benchmark data. 4TU.Centre for Research Data. https://doi.org/10.4121/uuid:b9f3df9e-08bc-4331-96da-7cabfa8970c0


Abstract Views: 213

PDF Views: 106




  • Application of Facebook's Prophet Algorithm for Successful Sales Forecasting Based on Real-World Data

Abstract Views: 213  |  PDF Views: 106

Authors

Emir Zunic
Info Studio d.o.o. Sarajevo, Bosnia and Herzegovina
Kemal Korjenic
Info Studio d.o.o. Sarajevo, Bosnia and Herzegovina
Kerim Hodzic
University of Sarajevo, Bosnia and Herzegovina
Dzenana Donko
University of Sarajevo, Bosnia and Herzegovina

Abstract


This paper presents a framework capable of accurately forecasting future sales in the retail industry and classifying the product portfolio according to the expected level of forecasting reliability. The proposed framework, that would be of great use for any company operating in the retail industry, is based on Facebook's Prophet algorithm and backtesting strategy. Real-world sales forecasting benchmark data obtained experimentally in a production environment in one of the biggest retail companies in Bosnia and Herzegovina is used to evaluate the framework and demonstrate its capabilities in a real-world use case scenario.

Keywords


Sales Forecasting, Real-World Dataset, Prophet, Backtesting, Classification.

References