LSTM (Long-short Term Memory)과 GRU (Gated Recurrent Units) 모델을 활용한 양식산 넙치 도매가격 예측 연구
Forecasting the Wholesale Price of Farmed Olive Flounder Paralichthys olivaceus Using LSTM and GRU Models
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Fluctuations in the price of aquaculture products have recently intensified. In particular, wholesale price fluctuations are adversely affecting consumers. Therefore, there is an emerging need for a study on forecasting the wholesale price of aquaculture products. The present study forecasted the wholesale price of olive flounder Paralichthys olivaceus, a representative farmed fish species in Korea, by constructing multivariate long-short term memory (LSTM) and gated recurrent unit (GRU) models. These deep learning models have recently been proven to be effective for forecasting in various fields. A total of 191 monthly data obtained for 17 variables were used to train and test the models. The results showed that the mean average percent error of LSTM and GRU models were 2.19% and 2.68%, respectively.
Abstract
Fluctuations in the price of aquaculture products have recently intensified. In particular, wholesale price fluctuations are adversely affecting consumers. Therefore, there is an emerging need for a study on forecasting the wholesale price of aquaculture products. The present study forecasted the wholesale price of olive flounder Paralichthys olivaceus, a representative farmed fish species in Korea, by constructing multivariate long-short term memory (LSTM) and gated recurrent unit (GRU) models. These deep learning models have recently been proven to be effective for forecasting in various fields. A total of 191 monthly data obtained for 17 variables were used to train and test the models. The results showed that the mean average percent error of LSTM and GRU models were 2.19% and 2.68%, respectively.
참고문헌 (41)
- Ayodele Ariyo Adebiyi, "Comparison of ARIMA and Artificial Neural Networks Models for Stock Price Prediction", Journal of Applied Mathematics, 2014, pp.614342~ (2014), Hindawi Limited[DOI]
- 배경태, "인공신경망의 은닉층 최적화를 통한 농산물 가격예측 모델", 한국정보기술학회논문지, 14(12), pp.161~169 (2016), 한국정보기술학회[DOI]
- "What is the Difference Between a Batch and an Epoch in a Neural Network?" (2018)[URL]
- "Learning phrase representations using RNN encoder-decoder for statistical machine translation" (2014)[URL]
- John C. B. Cooper, "Artificial neural networks versus multivariate statistics: An application from economics", Journal of Applied Statistics, 26(8), pp.909~921 (1999), Informa UK Limited[DOI]
- Arnaud de Myttenaere, "Mean Absolute Percentage Error for regression models", Neurocomputing, 192, pp.38~48 (2016), Elsevier BV[DOI]
- Fu R, "Using LSTM and GRU Neural Network Methods for Traffic Flow Prediction", 31st Youth Academic Annual Conference of Chinese Association of Automation (YAC), pp.324~328 (2016)
- Paul Goodwin, "On the asymmetry of the symmetric MAPE", International Journal of Forecasting, 15(4), pp.405~408 (1999), Elsevier BV[DOI]
- Daniel V. Gordon, "Price modelling in the Canadian fish supply chain with forecasts and simulations of the producer price of fish", Aquac Econ Manag, 21(1), pp.105~124 (2017), Informa UK Limited[DOI]
- Atle G. Guttormsen, "Forecasting weekly salmon prices: Risk management in fish farming", Aquac Econ Manag, 3(2), pp.159~166 (1999), Informa UK Limited[DOI]
- Sepp Hochreiter, "Long Short-Term Memory", Neural Computation, 9(8), pp.1735~1780 (1997), MIT Press - Journals[DOI]
- 황강석, "인공신경망을 활용한 고등어의 위판가격 변동 예측 - 어획량 제한이 없었던 TAC제도 시행 이전의 경우 -", 수산해양기술연구, 48(1), pp.72~81 (2012), 한국수산해양기술학회
- 임진모, "인공지능을 이용한 과일 가격 예측 모델 연구", 문화기술의 융합, 4(2), pp.197~204 (2018), 국제문화기술진흥원[DOI]
- 강석규, "소비 대체 양식어종 간의 가격 인과성과 변동성 전이에 관한 연구", 수산경영론집, 46(3), pp.119~127 (2015), 한국수산경영학회[DOI]
- Kingma D, "Adam: A method for stochastic optimization", International Conference on Learning Representations (ICLR) (2015)
- Fisheries Consumption Behavior, "Kim N", Korea Maritime Institute, 15~30 (2019)
- "National Climate and Weather Database. Retrieved from"[URL]
- "Fisheries Outlook Statistics" (2022)[URL]
- 고봉현, "수산물 시장에서의 양식어류 가격변동성·계절성·요일효과에 관한 연구 －노량진수산시장의 넙치와 조피볼락을 중심으로－", 수산경영론집, 40(2), pp.49~70 (2009), 한국수산경영학회
- "Survey on Fishery Processing Industry" (2022)[URL]
- KREI (Korea Rural Economic Institute), "2020 Food Balance Sheet", KREI, ~ (2022)
- "Trade Statistics" (2022)[URL]
- Le Q, "On optimization methods for deep learning", Proceedings of the 28th International Conference on International Conference on Machine Learning (2011)
- 이덕찬, "수온 변동 자극이 양식 넙치, Paralichthys olivaceus의비특이적 생체 방어에 미치는 영향", 한국어병학회지, 15(2), pp.65~75 (2002), 한국어병학회
- 이가현, "딥러닝 기법을 활용한 양식산 조피볼락 산지가격 예측 연구 －LSTM(Long-short Term Memory)과 GRU(Gated Recurrent Units) 모델을 중심으로－", 식품유통연구, 39(4), pp.21~41 (2022), 한국식품유통학회
- 이헌동, "양식넙치 산지-도매가격간 비대칭적 가격전이 분석", 수산경영론집, 51(4), pp.69~83 (2020), 한국수산경영학회[DOI]
- 이정미, "수산물 시장의 유통단계별 가격전달의 비대칭성에 관한 실증 분석", 수산경영론집, 41(3), pp.59~78 (2010), 한국수산경영학회
- 2020 A Survey on the Status of Marine Products Production and Distribution Industry, "MOF (Ministry of Oceans and Fisheries)", MOF, ~ (2021)
- Mun SE, "Machine learning and deep learning technology trends", Inform Commun Magaz, 33, pp.49~56 (2016)
- Maryam M Najafabadi, "Deep learning applications and challenges in big data analytics", Journal of Big Data, 2(1), pp.1~ (2015), Springer Science and Business Media LLC[DOI]
- 남종오, "시계열 모형을 이용한 김 위판가격 예측에 관한 연구", 해양정책연구, 29(2), pp.271~303 (2014), 한국해양수산개발원[DOI]
- 남종오, "제주 양식넙치의 월별 산지가격 예측 및 예측력 비교", 해양정책연구, 32(2), pp.1~22 (2017), 한국해양수산개발원[DOI]
- Selvin S, "Stock price prediction using LSTM, RNN and CNN-sliding window model", 2017 International Conference on Advances in Computing, Communications and In-formatics (ICACCI), pp.1643~1647 (2017)
- Farah Shahid, "Predictions for COVID-19 with deep learning models of LSTM, GRU and Bi-LSTM", Chaos Solit Fractals, 140, pp.110212~ (2020), Elsevier BV[DOI]
- 신성호, "LSTM 네트워크를 활용한 농산물 가격 예측 모델", 한국콘텐츠학회 논문지, 18(11), pp.416~429 (2018), 한국콘텐츠학회[DOI]
- Song YN, "A study on predicting the producer price of farmed olive flounder in Jeju Island using artificial neural network models", M.S, pp.2021
- Cyril Voyant, "Machine learning methods for solar radiation forecasting: A review", Renewable Energy, 105, pp.569~582 (2017), Elsevier BV[DOI]
- Huaizhi Wang, "A review of deep learning for renewable energy forecasting", Energy Conversion and Management, 198, pp.111799~ (2019), Elsevier BV[DOI]
- Michael K. Wohlgenant, "Competitive Storage, Rational Expectations, and Short‐Run Food Price Determination", American Journal of Agricultural Economics, 67(4), pp.739~748 (1985), Wiley[DOI]
- 우경원, "ARMA모형과 머신러닝기법을 활용한 조피볼락 가격 예측", 수산해양교육연구, 34(3), pp.415~426 (2022), 한국수산해양교육학회[DOI]
- 윤성주, "인공 신경망을 이용한 국제곡물가격예측 모형 개발", 농업경제연구, 57(2), pp.83~101 (2016), 한국농업경제학회