Research output: Contribution to journal › Conference article › peer-review
Framework for automated food export gain forecasting. / Devyatkin, Dmitry; Otmakhova, Yulia.
In: CEUR Workshop Proceedings, Vol. 2523, 01.01.2019, p. 22-33.Research output: Contribution to journal › Conference article › peer-review
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TY - JOUR
T1 - Framework for automated food export gain forecasting
AU - Devyatkin, Dmitry
AU - Otmakhova, Yulia
PY - 2019/1/1
Y1 - 2019/1/1
N2 - The food and agriculture could be a driver of the economy in Russia if intensive growth factors were mainly used. In particular, it is necessary to adjust the food export structure to fit reality better. This problem implies long-term forecasting of the commodity combinations and export directions which could provide a persistent export gain in the future. Unfortunately, the existing solutions for food market forecasting tackle mainly with short-term prediction, whereas structural changes in a whole branch of an economy can last during years. Long-term food market forecasting is a tricky one because food markets are quite unstable and export values depend on a variety of different features. The paper provides a multi-step data-driven framework which uses multimodal data from various databases to detect these commodities and export directions. We propose the quantile nonlinear autoregressive exogenous model together with pre-filtering to tackle with such long-term prediction tasks. The framework also considers textual information from mass-media to assess political risks related to prospective export directions. The experiments show that the proposed framework provides more accurate predictions then widely used ARIMA model. The expert validation of the obtained result confirms that the framework could be useful for export diversification.
AB - The food and agriculture could be a driver of the economy in Russia if intensive growth factors were mainly used. In particular, it is necessary to adjust the food export structure to fit reality better. This problem implies long-term forecasting of the commodity combinations and export directions which could provide a persistent export gain in the future. Unfortunately, the existing solutions for food market forecasting tackle mainly with short-term prediction, whereas structural changes in a whole branch of an economy can last during years. Long-term food market forecasting is a tricky one because food markets are quite unstable and export values depend on a variety of different features. The paper provides a multi-step data-driven framework which uses multimodal data from various databases to detect these commodities and export directions. We propose the quantile nonlinear autoregressive exogenous model together with pre-filtering to tackle with such long-term prediction tasks. The framework also considers textual information from mass-media to assess political risks related to prospective export directions. The experiments show that the proposed framework provides more accurate predictions then widely used ARIMA model. The expert validation of the obtained result confirms that the framework could be useful for export diversification.
KW - Data-driven market forecasting
KW - International trade
KW - Multimodal data
KW - Quantile regression
UR - http://www.scopus.com/inward/record.url?scp=85077499797&partnerID=8YFLogxK
M3 - Conference article
AN - SCOPUS:85077499797
VL - 2523
SP - 22
EP - 33
JO - CEUR Workshop Proceedings
JF - CEUR Workshop Proceedings
SN - 1613-0073
T2 - 21st International Conference on Data Analytics and Management in Data Intensive Domains, DAMDID/RCDL 2019
Y2 - 15 October 2019 through 18 October 2019
ER -
ID: 23110652