Volume 13, Number 2

Comparison Analysis of Facebook’s Prophet, Amazon’s DeepAR+ and CNN-QR Algorithms
for Successful Real-World Sales Forecasting


Emir Žunić1, 2, Kemal Korjenić1, Sead Delalić1, 2 and Zlatko Šubara1, 1Info Studio d.o.o. Sarajevo, Bosnia and Herzegovina, 2University of Sarajevo, Bosnia and Herzegovina


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.


Sales forecasting, Real-world dataset, Prophet, DeepAR+, CNN-QR, Backtesting, Classification.