Journal of the Korean Society for Industrial and Applied Mathematics

  • 제26권1호
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  • Pages.23-48
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  • 2022
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  • 1226-9433(pISSN)
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  • 1229-0645(eISSN)
한국산업응용수학회 (Korean Society for Industrial and Applied Mathematics)
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PREDICTING KOREAN FRUIT PRICES USING LSTM ALGORITHM

  • PARK, TAE-SU (DEPARTMENT OF MATHEMATICS, HANKUK UNIVERSITY OF FOREIGN STUDIES) ;
  • KEUM, JONGHAE (SCHOOL OF MATHEMATICS, KOREA INSTITUTE FOR ADVANCED STUDY) ;
  • KIM, HOISUB (SCHOOL OF MATHEMATICS, KOREA INSTITUTE FOR ADVANCED STUDY) ;
  • KIM, YOUNG ROCK (SCHOOL OF MATHEMATICS, KOREA INSTITUTE FOR ADVANCED STUDY) ;
  • MIN, YOUNGHO (DEPARTMENT OF MATHEMATICS EDUCATION, HANKUK UNIVERSITY OF FOREIGN STUDIES)
  • 투고 : 2021.12.30
  • 심사 : 2022.03.24
  • 발행 : 2022.03.25
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초록

In this paper, we provide predictive models for the market price of fruits, and analyze the performance of each fruit price predictive model. The data used to create the predictive models are fruit price data, weather data, and Korea composite stock price index (KOSPI) data. We collect these data through Open-API for 10 years period from year 2011 to year 2020. Six types of fruit price predictive models are constructed using the LSTM algorithm, a special form of deep learning RNN algorithm, and the performance is measured using the root mean square error. For each model, the data from year 2011 to year 2018 are trained to predict the fruit price in year 2019, and the data from year 2011 to year 2019 are trained to predict the fruit price in year 2020. By comparing the fruit price predictive models of year 2019 and those models of year 2020, the model with excellent efficiency is identified and the best model to provide the service is selected. The model we made will be available in other countries and regions as well.

키워드

과제정보

Tae-Su Park and Young Rock Kim were supported by Hankuk University of Foreign Studies Research Fund of 2021. JongHae Keum and Hoisub Kim were supported by KIAS Individual Grant (MG008512) at Korea Institute for Advanced Study. Young Rock Kim and Youngho Min were supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2021R1A2C1011467).

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