  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.
