Journal of the Korean Society for Industrial and Applied Mathematics

Korean Society for Industrial and Applied Mathematics (한국산업응용수학회)
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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)
  • Received : 2021.12.30
  • Accepted : 2022.03.24
  • Published : 2022.03.25
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Abstract

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.

Keywords

Acknowledgement

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).

References

  1. Weisong, M., Xiaoshuan, Z., Lingxian, Z., & Zettan, F., A structural model for analysis of fruit supply and demand applied to grapes in China, New Zealand Journal of Agricultural Research, 50(5) (2007), 1359-1365. https://doi.org/10.1080/00288230709510423
  2. Czyzewski, A., Bieniek-Majka, M., & Czakowski, D., Factors shaping supply-demand relations on the fruit and vegetable market in the light of the behavior of groups and producer organizations, Management, 22(1) (2018).
  3. Oeasby, R. C., Darroch, M. A., & Dushmaniteh, V. Y., The demand for and supply of South African deciduous fruit exports: a dynamic analysis., Agrekon, 30(4) (1991), 241-243. https://doi.org/10.1080/03031853.1991.9524243
  4. Weerahewa, J., Rajapakse, C., & Pushpakumara, G., An analysis of consumer demand for fruits in Sri Lanka, Appetite, 60 (1981-2010), 252-258.
  5. Chang, J. H., Kim, J. W., Kwak, D. E., & Aziz, N., A Correlation Study Between Fruit Wholesale Price And Weather Factor. In Proceedings of the Korea Information Processing Society Conference., Korea Information Processing Society, (2017), 706-708.
  6. Ho, S. T., Ifft, J. E., Rickard, B. J., & Turvey, C. G. , Alternative strategies to manage weather risk in perennial fruit crop production., Agricultural and Resource Economics Review, 47(3) (2018), 452-476. https://doi.org/10.1017/age.2017.29
  7. Dong, D., & Lin, B. H., Fruit and vegetable consumption by low-income Americans: would a price reduction make a difference?, 60, (2009), No. 1477-2016-121112.
  8. Rickard, B. J., & Lei, L. , How important are tariffs and nontariff barriers in international markets for fresh fruit?., Agricultural Economics, 42 (2011), 19-32. https://doi.org/10.1111/j.1574-0862.2011.00549.x
  9. Cummings Jr, R., Rashid, S., & Gulati, A. , Grain price stabilization experiences in Asia: What have we learned?, Food Policy, 31(4) (2006), 302-312. https://doi.org/10.1016/j.foodpol.2006.03.006
  10. Im, J. M., Kim, W. Y., Byoun, W. J., & Shin, S. J., Fruit price prediction study using artificial intelligence., Journal of the convergence on culture technology, Vol. 4, No. 2, pp.197-204, May 31, (2018), pISSN 2384-0358, eISSN 2384-0366. https://doi.org/10.17703/JCCT.2018.4.2.197
  11. Shin, S., Lee, M., & Song, S. K., A Prediction Model for Agricultural Products Price with LSTM Network. The Journal of the Korea Contents Association, 18(11) (2018), 416-429. https://doi.org/10.5392/JKCA.2018.18.11.416
  12. Schmidhuber, J., Deep learning in neural networks: An overview. Neural networks, New Zealand Journal of Agricultural Research, 61 (2015), 85-117.
  13. Schmidhuber J., WebCite, Demonstrates Credit Assignment Across the Equivalent of 1,200 Layers in an Unfolded RNN., URL: http://www.webcitation.org/71i6G4Jaw [WebCite Cache ID 71i6G4Jaw] (1993).
  14. Bengio, Y., Frasconi, P., & Simard, P., The problem of learning long-term dependencies in recurrent networks, IEEE international conference on neural networks (pp. 1183-1188). IEEE. (1993).
  15. Raschka, S., & Mirjalili, V., Python Machine Learning: Machine Learning and Deep Learning with Python. Scikit-Learn, and TensorFlow. Second edition ed. (2017).
  16. Hochreiter, S., & Schmidhuber, J., Long short-term memory, Neural computation, 9(8) (1997), 1735-1780. https://doi.org/10.1162/neco.1997.9.8.1735
  17. Gers, F. A., Schmidhuber, J., & Cummins, F., Learning to forget: Continual prediction with LSTM, Neural computation, 12(10) (2000), 2451-2471. https://doi.org/10.1162/089976600300015015
  18. Vinyals, O., Toshev, A., Bengio, S., & Erhan, D. Show and tell: A neural image caption generator, In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 3156-3164). IEEE. (2015).
  19. Joo, I. T., & Choi, S. H., Stock prediction model based on bidirectional LSTM recurrent neural network, The Journal of Korea Institute of Information, Electronics, and Communication Technology, 11(2) (2018), 204-208. https://doi.org/10.17661/JKIIECT.2018.11.2.204
  20. Wu, Yonghui, et al., Google's neural machine translation system: Bridging the gap between human and machine translation, arXiv preprint arXiv:1609.08144, (2016).
  21. Kyoung-woo Cho, Yong-jin Jung, Chul-gyu Kang, Chang-heon Oh, Conformity Assessment of Machine Learning Algorithm for Particulate Matter Prediction, Journal of the Korea Institute of Information and Communication Engineering,(2019), Vol. 23, No. 1: 20-26. https://doi.org/10.6109/JKIICE.2019.23.1.20
  22. FTA Implementation Support Center, Agricultural and livestock export and import trend Vol. 8, No. 3, Korea Rural Economic Institute, (2020).
  23. FTA Implementation Support Center, Agricultural and livestock export and import trend Vol. 8, No. 4, Korea Rural Economic Institute, (2020).