Standard
Neural Networks for Food Export Gain Forecasting. / Devyatkin, Dmitry; Suvorov, Roman; Tikhomirov, Ilya et al.
9th International Conference on Intelligent Systems 2018: Theory, Research and Innovation in Applications, IS 2018 - Proceedings. ed. / Joao Martins; Vladimir Jotsov; Robert Bierwolf; Joao Pedro Mendonca; Ricardo Jardim-Goncalves; Maria Marques. Institute of Electrical and Electronics Engineers Inc., 2018. p. 312-317 8710561 (9th International Conference on Intelligent Systems 2018: Theory, Research and Innovation in Applications, IS 2018 - Proceedings).
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review
Harvard
Devyatkin, D, Suvorov, R, Tikhomirov, I & Otmakhova, Y 2018,
Neural Networks for Food Export Gain Forecasting. in J Martins, V Jotsov, R Bierwolf, JP Mendonca, R Jardim-Goncalves & M Marques (eds),
9th International Conference on Intelligent Systems 2018: Theory, Research and Innovation in Applications, IS 2018 - Proceedings., 8710561, 9th International Conference on Intelligent Systems 2018: Theory, Research and Innovation in Applications, IS 2018 - Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 312-317, 9th International Conference on Intelligent Systems, IS 2018, Funchal - Madeira, Portugal,
25.09.2018.
https://doi.org/10.1109/IS.2018.8710561APA
Devyatkin, D., Suvorov, R., Tikhomirov, I., & Otmakhova, Y. (2018).
Neural Networks for Food Export Gain Forecasting. In J. Martins, V. Jotsov, R. Bierwolf, J. P. Mendonca, R. Jardim-Goncalves, & M. Marques (Eds.),
9th International Conference on Intelligent Systems 2018: Theory, Research and Innovation in Applications, IS 2018 - Proceedings (pp. 312-317). [8710561] (9th International Conference on Intelligent Systems 2018: Theory, Research and Innovation in Applications, IS 2018 - Proceedings). Institute of Electrical and Electronics Engineers Inc..
https://doi.org/10.1109/IS.2018.8710561Vancouver
Devyatkin D, Suvorov R, Tikhomirov I, Otmakhova Y.
Neural Networks for Food Export Gain Forecasting. In Martins J, Jotsov V, Bierwolf R, Mendonca JP, Jardim-Goncalves R, Marques M, editors, 9th International Conference on Intelligent Systems 2018: Theory, Research and Innovation in Applications, IS 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2018. p. 312-317. 8710561. (9th International Conference on Intelligent Systems 2018: Theory, Research and Innovation in Applications, IS 2018 - Proceedings). doi: 10.1109/IS.2018.8710561
Author
Devyatkin, Dmitry ; Suvorov, Roman ; Tikhomirov, Ilya et al. /
Neural Networks for Food Export Gain Forecasting. 9th International Conference on Intelligent Systems 2018: Theory, Research and Innovation in Applications, IS 2018 - Proceedings. editor / Joao Martins ; Vladimir Jotsov ; Robert Bierwolf ; Joao Pedro Mendonca ; Ricardo Jardim-Goncalves ; Maria Marques. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 312-317 (9th International Conference on Intelligent Systems 2018: Theory, Research and Innovation in Applications, IS 2018 - Proceedings).
BibTeX
@inproceedings{c09054d2d3454f2fbd0ece5b4eb39dcb,
title = "Neural Networks for Food Export Gain Forecasting",
abstract = "Agriculture and food production could be an engine of economic growth in a lot of countries. The obvious way to force food and agriculture development is to discover new foreign markets with high probability of growth in the nearest future. The objective of this study is to reveal combinations of commodities and partner countries for which persistent growth of export value is expected. The proposed framework uses open data about international trade flows and production from United Nations, International Monetary Found, weather databases and quantile regression models based on neural networks. The experiments show that considering retrospective data allows to accurate forecast the desired combinations.",
keywords = "Data mining, Food market, International trade, Machine learning, Neural networks, Open data, Quantile regression",
author = "Dmitry Devyatkin and Roman Suvorov and Ilya Tikhomirov and Yulia Otmakhova",
year = "2018",
month = jul,
day = "2",
doi = "10.1109/IS.2018.8710561",
language = "English",
series = "9th International Conference on Intelligent Systems 2018: Theory, Research and Innovation in Applications, IS 2018 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "312--317",
editor = "Joao Martins and Vladimir Jotsov and Robert Bierwolf and Mendonca, {Joao Pedro} and Ricardo Jardim-Goncalves and Maria Marques",
booktitle = "9th International Conference on Intelligent Systems 2018",
address = "United States",
note = "9th International Conference on Intelligent Systems, IS 2018 ; Conference date: 25-09-2018 Through 27-09-2018",
}
RIS
TY - GEN
T1 - Neural Networks for Food Export Gain Forecasting
AU - Devyatkin, Dmitry
AU - Suvorov, Roman
AU - Tikhomirov, Ilya
AU - Otmakhova, Yulia
PY - 2018/7/2
Y1 - 2018/7/2
N2 - Agriculture and food production could be an engine of economic growth in a lot of countries. The obvious way to force food and agriculture development is to discover new foreign markets with high probability of growth in the nearest future. The objective of this study is to reveal combinations of commodities and partner countries for which persistent growth of export value is expected. The proposed framework uses open data about international trade flows and production from United Nations, International Monetary Found, weather databases and quantile regression models based on neural networks. The experiments show that considering retrospective data allows to accurate forecast the desired combinations.
AB - Agriculture and food production could be an engine of economic growth in a lot of countries. The obvious way to force food and agriculture development is to discover new foreign markets with high probability of growth in the nearest future. The objective of this study is to reveal combinations of commodities and partner countries for which persistent growth of export value is expected. The proposed framework uses open data about international trade flows and production from United Nations, International Monetary Found, weather databases and quantile regression models based on neural networks. The experiments show that considering retrospective data allows to accurate forecast the desired combinations.
KW - Data mining
KW - Food market
KW - International trade
KW - Machine learning
KW - Neural networks
KW - Open data
KW - Quantile regression
UR - http://www.scopus.com/inward/record.url?scp=85065967127&partnerID=8YFLogxK
U2 - 10.1109/IS.2018.8710561
DO - 10.1109/IS.2018.8710561
M3 - Conference contribution
AN - SCOPUS:85065967127
T3 - 9th International Conference on Intelligent Systems 2018: Theory, Research and Innovation in Applications, IS 2018 - Proceedings
SP - 312
EP - 317
BT - 9th International Conference on Intelligent Systems 2018
A2 - Martins, Joao
A2 - Jotsov, Vladimir
A2 - Bierwolf, Robert
A2 - Mendonca, Joao Pedro
A2 - Jardim-Goncalves, Ricardo
A2 - Marques, Maria
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 9th International Conference on Intelligent Systems, IS 2018
Y2 - 25 September 2018 through 27 September 2018
ER -
