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Comparison Analysis of Facebook’s Prophet, Amazon’s DEEPAR+ AND CNN-QR Algorithms for Successful Real-World Sales Forecasting
By successfully solving the problem of forecasting, the processes in the work of various companies are optimized and savings are achieved. In this process, the analysis of time series data is of particular importance. Since the creation of Facebook’s Prophet, and Amazon’s DeepAR+ and CNN-QR forecasting models, algorithms have attracted a great deal of attention. The paper presents the application and comparison of the above algorithms for sales forecasting in distribution companies. A detailed comparison of the performance of algorithms over real data with different lengths of sales history was made. The results show that Prophet gives better results for items with a longer history and frequent sales, while Amazon’s algorithms show superiority for items without a long history and items that are rarely sold.
Keywords
Sales Forecasting, Real-World Dataset, Prophet, DeepAR+, CNN-QR, Backtesting, Classification.
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- Bajrić, H. (2020). Zalihe - Koliko Vaš biznis košta to što ste prezauzeti?. LinkedIn. Online source. (accessed 17.04.2021.). https://www.linkedin.com/pulse/zalihe-koliko-vaš-biznis-košta-što-ste-prezauzeti-hadis-bajric/?articleId=6662479933127962624
- 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
- Žunić, E., Korjenić, K., Hodžić, K., and Đonko, D. (2020). Application of Facebook’s Prophet Algorithm for Successful Sales Forecasting Based on Real-world Data. International Journal of Computer Science and Information Technology (IJCSIT) 12(2): 23-36. https://doi.org/10.5121/ijcsit.2020.12203
- Žunić, E (2021). Real-world sales forecasting benchmark data - Extended version. 4TU.ResearchData. Dataset. https://doi.org/10.4121/14406134
- Dwivedi, A., Niranjan, M., and Sahu, K. A. (2013). Business Intelligence Technique for Forecasting the Automobile Sales using Adaptive Intelligent Systems (ANFIS and ANN). International Journal of Computer Applications 74(9): 7-13. https://doi.org10.5120/12911-9383
- Aburto, L., and Weber, R. (2007). Improved supply chain management based on hybrid demand forecasts. Applied Soft Computing 7(1): 136-144. https://doi.org/10.1016/j.asoc.2005.06.001
- Aras, S., Deveci Kocakoç, İ., and Polat, C. (2017). Comparative study on retail sales forecasting between single and combination methods. Journal of Business Economics and Management. https://doi.org/10.3846/16111699.2017.1367324
- Alon, I., Qi, M., and Sadowski, R. J. (2001). Forecasting aggregate retail sales: A comparison of artifcial 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
- Kang, S. (1991). An investigation of the use of feedforward neural networks for forecasting. Ph.D. Dissertation, Kent State University, Kent, Ohio
- 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
- Kolassa, S. (2016). Evaluating predictive count data distributions in retail sales forecasting. International Journal of Forecasting 32: 788-803. https://doi.org/10.1016/j.ijforecast.2015.12.004
- Jiménez, F., Sánchez, G., García, J. M., Sciavicco, G., and Mi-ralles, 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
- 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
- Salinas, D., Flunkert, V., Gasthaus, J., and Januschowski, T. (2020). DeepAR: Probabilistic forecasting with autoregressive recurrent networks. International Journal of Forecasting. 36(3): 1181-1191. https://doi.org/10.1016/j.ijforecast.2019.07.001
- 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. https://doi.org/10.1109/NEUREL.2018.8587004
- 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. https://doi.org/10.1109/ICAT.2017.8171629
- Zunic, E., Hasic, H., Hodzic, K., Delalic, S., and Besirevic, A. (2018). Predictive analysis based approach for optimal warehouse product positioning. in 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics, MIPRO. https://doi.org/10.23919/MIPRO.2018.8400174
- Žunić, E., Delalić, S., and Đonko, D. (2020). Adaptive multi-phase approach for solving the realistic vehicle routing problems in logistics with innovative comparison method for evaluation based on real GPS data. Transportation Letters. https://doi.org/10.1080/19427867.2020.1824311
- 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
- Žunić, E., Kuric, A., and Delalić, S. (2020). Improving unloading time prediction for Vehicle Routing Problem based on GPS data. in Position Papers of the 2020 Federated Conference on Computer Science and Information Systems, FedCSIS. https://doi.org/10.15439/2020f123
- 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. https://doi.org/10.15439/2019F19
- Žunić, E., Delalić, S., Hodžić, K., and Tucaković, Z. (2019). Innovative GPS Data Anomaly Detection Algorithm inspired by QRS Complex Detection Algorithms in ECG Signals. in EUROCON 2019 - 18th International Conference on Smart Technologies. https://doi.org/10.1109/EUROCON.2019.8861619
- Zunic, E., Tucakovic, Z., Delalic, S., Hasic, H., and Hodzic, K. (2020). Innovative Multi-Step Anomaly Detection Algorithm with Real-World Implementation: Case Study in Supply Chain Management. in 2020 IEEE / ITU International Conference on Artificial Intelligence for Good, AI4G. https://doi.org/10.1109/AI4G50087.2020.9311045
- Žunić, E., Djedović, A., and Donko, D. (2016). Application of Big Data and text mining methods and technologies in modern business analyzing social networks data about traffic tracking. in 2016 11th International Symposium on Telecommunications, BIHTEL. https://doi.org/10.1109/BIHTEL.2016.7775717
- 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
- Harvey, A., and Peters, S. (1990). Estimation procedures for structural time series models. Journal of Forecasting 9: 89-108. https://doi.org/10.1002/for.3980090203
- Harvey, A. C., and Shephard, N. (1993). Structural time series models, in G. Maddala, C. Rao & H. Vinod, eds, ‘Handbook of Statistics’ 11: 261-302. https://doi.org/10.1016/S0169-7161(05)80045-8
- AWS documentation. (2021). DeepAR+ Algorithm. Online (accessed 17.04.2021.). https://docs.aws.amazon.com/forecast/latest/dg/aws-forecast-recipe-deeparplus.html
- AWS documentation. (2021). CNN-QR Algorithm. Online (accessed 17.04.2021.). https://docs.aws.amazon.com/forecast/latest/dg/aws-forecast-algo-cnnqr.html
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