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Towards framework for discovery of export growth points. / Devyatkin, Dmitry; Suvorov, Roman; Tikhomirov, Ilya et al.

In: CEUR Workshop Proceedings, Vol. 2022, 01.01.2017, p. 142-147.

Research output: Contribution to journal › Conference article › peer-review

Harvard

Devyatkin, D, Suvorov, R, Tikhomirov, I & Otmakhova, Y 2017, 'Towards framework for discovery of export growth points', CEUR Workshop Proceedings, vol. 2022, pp. 142-147.

APA

Devyatkin, D., Suvorov, R., Tikhomirov, I., & Otmakhova, Y. (2017). Towards framework for discovery of export growth points. CEUR Workshop Proceedings, 2022, 142-147.

Vancouver

Devyatkin D, Suvorov R, Tikhomirov I, Otmakhova Y. Towards framework for discovery of export growth points. CEUR Workshop Proceedings. 2017 Jan 1;2022:142-147.

Author

Devyatkin, Dmitry ; Suvorov, Roman ; Tikhomirov, Ilya et al. / Towards framework for discovery of export growth points. In: CEUR Workshop Proceedings. 2017 ; Vol. 2022. pp. 142-147.

BibTeX

@article{b9fd892a44f3412183d530212f31f7d1,
title = "Towards framework for discovery of export growth points",
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.",
keywords = "Customs statistics, Data mining, Export growth potential, International trade, Machine learning, Open data",
author = "Dmitry Devyatkin and Roman Suvorov and Ilya Tikhomirov and Yulia Otmakhova",
note = "Funding Information: The research is supported by Russian Foundation for Basic Research, project 16-29-12877.",
year = "2017",
month = jan,
day = "1",
language = "English",
volume = "2022",
pages = "142--147",
journal = "CEUR Workshop Proceedings",
issn = "1613-0073",
publisher = "CEUR-WS",

}

RIS

TY - JOUR

T1 - Towards framework for discovery of export growth points

AU - Devyatkin, Dmitry

AU - Suvorov, Roman

AU - Tikhomirov, Ilya

AU - Otmakhova, Yulia

N1 - Funding Information: The research is supported by Russian Foundation for Basic Research, project 16-29-12877.

PY - 2017/1/1

Y1 - 2017/1/1

N2 - 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.

AB - 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.

KW - Customs statistics

KW - Data mining

KW - Export growth potential

KW - International trade

KW - Machine learning

KW - Open data

UR - http://www.scopus.com/inward/record.url?scp=85040659603&partnerID=8YFLogxK

M3 - Conference article

AN - SCOPUS:85040659603

VL - 2022

SP - 142

EP - 147

JO - CEUR Workshop Proceedings

JF - CEUR Workshop Proceedings

SN - 1613-0073

ER -

ID: 12693509