Export value of the Russian Federation has been reducing in the latest years, as well as the corresponding relative yield. Most probably, this trend is caused by Russia total export decline together with growth of food export. Thus, it is very important to not only increase export volumes, but also adjust export structure to fit nowadays reality better. The paper presents a computer-aided framework for export growth points discovery. While the full framework is described briefly, more attention is paid to the first sub-task: growth point candidates ranking. The objective of this sub-task is to reveal combinations of commodities and partner countries with high probability of successful export. The method uses open data about international trade flows and production from United Nations databases and modern machine learning methods. The experimental evaluation shows that taking into account retrospective data allows ranking growth point candidates significantly better. Finally, the limitations and the possible directions of future research are discussed.
|Журнал||CEUR Workshop Proceedings|
|Состояние||Опубликовано - 1 янв 2017|