The article presents a comparative analysis of the forecasting accuracy of five methods for demand and price time series: classical statistical approaches, machine learning algorithms, and a deep learning architecture.

Keywords: time series forecasting, road freight transportation, machine learning, gradient boosting, recurrent neural networks, comparative analysis of forecasting methods