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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. NTRODUCTION Successful sales forecasting mechanisms can have positive effects in many areas of business, and one of the basic aspects is stock optimization. In retail, wholesale and distribution companies, inventory optimization is one of the key aspects of business. Companies that maintain their stocks at an adequate and satisfactory level can save significant amounts of money, and at the same time their other processes, such as warehousing, commissioning, shipping, etc. are significantly improved. Stock optimization often does not have enough attention in a real environment. According to the detailed analysis presented by Bajrić [1], inventory management in the average company from Bosnia and Herzegovina is far from satisfactory. There are either too many products in the stock, so there is an unnecessary cost of keeping them, or not enough products, so there is a lost sales, cost of stopping production, replanting, switching to other products, breaking deadlines, returning to production of the original product and related costs. According to the mentioned research, stocks in the average Bosnian company can be reduced by an average of 25%.

Global demographic and economic changes have a critical impact on the total energy consumption, which is why demographic and economic parameters have to be taken into account when making predictions about the energy consumption. This research is based on the application of a multiple linear regression model and a neural network model, in particular multilayer perceptron, for predicting the energy consumption. Data from five Balkan countries has been considered in the analysis for the period 1995-2014. Gross domestic product, total number of population, and CO2 emission were taken as predictor variables, while the energy consumption was used as the dependent variable. The analyses showed that CO2 emissions have the highest impact on the energy consumption, followed by the gross domestic product, while the population number has the lowest impact. The results from both analyses are then used for making predictions on the same data, after which the obtained values were compared with the real values. It was observed that the multilayer perceptron model predicts better the energy consumption than the regression model.