In the master's thesis, we developed models for short-term forecasting of ferroalloy prices, which are used in the steel industry for the production of high-alloy steel. For the development of forecasting models, we employed ensemble methods, such as the random forests method, in combination with the analysis of autocorrelation functions of time series. For comparison, we also utilized the method of recurrent neural networks. The performance of the models was evaluated by comparing them to a baseline model that assumes no changes in price. Additionally, we performed hyperparameter optimization to further improve the models' performance. Our findings revealed that the forecasting models did not perform significantly better than the baseline model that assumes no price changes. One of the reasons is that the developed models do not incorporate additional variables or factors, such as the prices of other raw materials, global production levels, inventories of high-alloy steel, and energy prices, which are important for alloy steel production. If the forecasting model were to be used in practice, it would need to be enhanced by including additional variables.
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