Predicting the Future Price of Export Items in Trade Using a Deep Regression Model


KIPS Transactions on Software and Data Engineering, Vol. 11, No. 10, pp. 427-436, Oct. 2022
https://doi.org/10.3745/KTSDE.2022.11.10.427,   PDF Download:
Keywords: KOTRA, BigData, Ministry of Trade Industry and Energy, Deep Learning, Multi Layer Perception
Abstract

Korea Trade-Investment Promotion Agency (KOTRA) annually publishes the trade data in South Korea under the guidance of the Ministry of Trade, Industry and Energy in South Korea. The trade data usually contains Gross domestic product (GDP), a custom tariff, business score, and the price of export items in previous and this year, with regards to the trading items and the countries. However, it is challenging to figure out the meaningful insight so as to predict the future price on trading items every year due to the significantly large amount of data accumulated over the several years under the limited human/computing resources. Within this context, this paper proposes a multi layer perception that can predict the future price of potential trading items in the next year by training large amounts of past year’s data with a low computational and human cost.


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Cite this article
[IEEE Style]
K. J. Hun and L. J. Hang, "Predicting the Future Price of Export Items in Trade Using a Deep Regression Model," KIPS Transactions on Software and Data Engineering, vol. 11, no. 10, pp. 427-436, 2022. DOI: https://doi.org/10.3745/KTSDE.2022.11.10.427.

[ACM Style]
Kim Ji Hun and Lee Jee Hang. 2022. Predicting the Future Price of Export Items in Trade Using a Deep Regression Model. KIPS Transactions on Software and Data Engineering, 11, 10, (2022), 427-436. DOI: https://doi.org/10.3745/KTSDE.2022.11.10.427.