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http://dx.doi.org/10.3745/KTSDE.2022.11.10.427

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

Kim, Ji Hun (상명대학교 휴먼지능정보공학전공)
Lee, Jee Hang (상명대학교 휴먼지능정보공학과)
Publication Information
KIPS Transactions on Software and Data Engineering / v.11, no.10, 2022 , pp. 427-436 More about this Journal
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.
Keywords
KOTRA; BigData; Ministry of Trade Industry and Energy; Deep Learning; Multi Layer Perception;
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