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 -