Towards framework for discovery of export growth points

Dmitry Devyatkin, Roman Suvorov, Ilya Tikhomirov, Yulia Otmakhova

Research output: Contribution to journalConference articlepeer-review

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)142-147
Number of pages6
JournalCEUR Workshop Proceedings
Volume2022
Publication statusPublished - 1 Jan 2017

Keywords

  • Customs statistics
  • Data mining
  • Export growth potential
  • International trade
  • Machine learning
  • Open data

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