Korea Trade Review (무역학회지)

Korea Trade Research Association (한국무역학회)
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Throughput Prediction of Pohang Port using Time Series Data: Application of SARIMA, Prophet and Neural Prophet

시계열 데이터를 활용한 포항항 물동량 예측: SARIMA, Prophet, Neural Prophet의 적용

  • Jin-Ho Oh (Department of International Trade, Graduate School, Kyungpook National University) ;
  • Jeong-Won Choi (Department of International Trade, Graduate School, Kyungpook National University) ;
  • Tae-Hyun Kang (Department of International Trade, Graduate School, Kyungpook National University) ;
  • Young-Joon Seo (Department of International Trade, Graduate School, Kyungpook National University) ;
  • Dong-Wook Kwak (Department of International Trade, Graduate School, Kyungpook National University)
  • 오진호 (경북대학교 대학원 무역학과) ;
  • 최정원 (경북대학교 대학원 무역학과) ;
  • 강태현 (경북대학교 대학원 무역학과) ;
  • 서영준 (경북대학교 대학원 무역학과) ;
  • 곽동욱 (경북대학교 대학원 무역학과)
  • Received : 2022.12.09
  • Accepted : 2022.12.27
  • Published : 2022.12.30
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Abstract

In this study, the volume of Pohang Port was predicted. All cargo of Pohang port, iron ore, steel, and bituminous coals were selected as prediction targets. SARIMA, Prophet, and Neural Prophet were used as analysis methods. The predictive power of each model was verified, and a predictive model with high performance was used to predict the volume of goods in Pohang port. As a result of the analysis, it was found that Neural Prophet showed the highest performance in all predictive power. As a result of predicting the future volume of goods until August 2027 using Neural Prophet, it was found that the volume of all items in Pohang port was decreasing. In particular, it was analyzed that the decline in steel cargo was steep. In order to increase the volume of cargo at Pohang port, it is necessary to diversify the cargo handled at Pohang port and check the policy of increasing the volume of cargo.

Keywords

Acknowledgement

This research was supported by the BK21 project funded by the Ministry of Education and National Research Foundation of Korea (4299990214398)

References

  1. Ahn Byung-Hoon, Hoe-Ryeon Choi and Hong-Chul Lee (2015), "Regional Long-term/Mid-term Load Forecasting using SARIMA in South Korea", Journal of the Korea Academia-Industrial cooperation Society, 16(12), 8576-8584. https://doi.org/10.5762/KAIS.2015.16.12.8576
  2. Battineni, G., N. Chintalapudi and F. Amenta (2020), "Forecasting of COVID-19 epidemic size in four high hitting nations (USA, Brazil, India and Russia) by Fb-Prophet machine learning model", Applied Computing and Informatics. https://doi.org/10.1108/ACI-09-2020-0059
  3. ChikkaKrishna, N. K., P. Rachakonda and T. Tallam (2022, February), "Short-Term Traffic Prediction Using Fb-PROPHET and Neural-PROPHET", In 2022 IEEE Delhi Section Conference (DELCON), 1-4. https://doi.org/10.1109/DELCON54057.2022.9753459
  4. Cho, Yang-Il and Seog-Soo Kim (2019), "A Study on The Workforce Agility and Operational Performance of Distribution Center - Focused on Busan New Port Distripark", Korea Trade Review, 44(3), 25-42. https://doi.org/10.22659/KTRA.2019.44.3.25
  5. Garlapati, A., D. R. Krishna, K. Garlapati, U. Rahul and G. Narayanan (2021, April), "Stock Price Prediction Using Facebook Prophet and Arima Models", In 2021 6th International Conference for Convergence in Technology (I2CT), 1-7. https://doi.org/10.1109/I2CT51068.2021.9418057
  6. Guo, L., W. Fang, Q. Zhao and X. Wang (2021), "The hybrid PROPHET-SVR approach for forecasting product time series demand with seasonality", Computers & Industrial Engineering, 161, 107598. https://doi.org/10.1016/j.cie.2021.107598
  7. Ha, Yeong-Seok and Hyuk-Soo Cho (2009), "Inducement of Firms in Container Port Hinterland: A Case of Pohang-Youngil Port", Journal of Shipping and Logistics, 25(1), 123-143. https://doi.org/10.37059/tjosal.2009.25.1.123
  8. Ha, Yeong-Seok and Jong-Gil Han (2006), "Winning Factors and Strategies for Regional Core Container Ports", Journal of Shipping and Logistics, 50, 1-20.
  9. Ham, Juh-Hyeok (2021), "A Study on the Prediction of Major Prices in the Shipbuilding Industry Using Time Series Analysis Model", Journal of the Society of Naval Architects of Korea, 58(5), 281-293. https://doi.org/10.3744/SNAK.2021.58.5.281
  10. Kim Jae-Ho and Jang-Young Kim (2021), "Covid19 trends predictions using time series data", Journal of the Korea Institute of Information and Communication Engineering, 25(7), 884-889. http://doi.org/10.6109/jkiice.2021.25.7.884
  11. Kim, Jun-Ki, Dong-Keun Ryoo and Hyung-Sik Nam (2022), "A Study on Forecasting of Small & Medium Sized Container Port Throughput in South Korea Using Prophet Model: Focused on Incheon, Pyeongtaek-Dangjin and Ulsan Port", The Journal of Humanities and Social science, 13(1), 561-576. https://doi.org/10.22143/HSS21.13.1.40
  12. Kim, Jun-Suk and Tae-Jin Kim (2021), "Application of Facebook's Prophet Model for Forecasting Meteorological Data", Journal of The Korean Society of Hazard Mitigation, 21(2), 53-58. https://doi.org/10.9798/KOSHAM.2021.21.2.53
  13. Kim, Jung-Hwan and Dong-Wook Kwak (2021), "A policy evaluation and empirical analysis on the business environments for facilitation of the Pohang Youngilman Port", Korea trade review, 46(6), 127-142. https://doi.org/10.22143/HSS21.13.1.40
  14. Koh, Yong-Ki, Jung-Yong Shin and Tae-Ho Kim (2008), "Forecasting Export Loaded Container Throughput of Pohang Yeongil New Port", Journal of Distribution and Management Research, 11(4), 107-132. https://doi.org/10.17961/JDMR.11.4.200812.107
  15. Kumar, N., R. Chauhan and G. Dubey (2021), "Forecasting of Stock Price Using LSTM and Prophet Algorithm", In Applications of Artificial Intelligence and Machine Learning, Singapore: Springer, 141-155. https://doi.org/10.1007/978-981-16-3067-5_12
  16. Kwak, Dong-Wook, Sung-Woo Lee, A-Rom Kim and Young-Joon Seo (2022), "A Study on vitalization of the Pohang Yeongil Port and its hinterland in the post Covid-19 pandemic", Journal of Korea Port Economic Association, 38(1), 73-86. https://doi.org/10.38121/kpea.2022.3.38.1.73
  17. Lee, Hyun-Soo, Seok-Won Lee, Mun-Jung Lee and Dong-Woo Lee (2022), "Predicting Rail Transport Demand by Reflecting Irregular Time Series Events (COVID-19)", Proceeding of the Journal of The Korea Institute of Intelligent Transportation Systems Conference, 531-537.
  18. Lee, Ji-Hoon, Hye-Rim Han and Sang-Hoo Yoon (2020), "Air passenger demand forecasting for the Incheon airport using time series models", Journal of Digital Convergence, 18(12), 87-95. https://doi.org/10.14400/JDC.2020.18.12.087
  19. Lee, M. H., N. H. Rahman, Suhartono, M. T. Latif, M. E. Nor and N. A. Kamisan (2012), "Seasonal ARIMA for forecasting air pollution index: A case study", American Journal of Applied Sciences, 9(4), 570-578. https://doi.org/10.3844/ajassp.2012.570.578
  20. Na, Ho-Su, U Lee and Gyeong-Su Lee (2008), "The Comparative Study on the Efficiency of Five Largest Seaports in Korea", Journal of Korea Port Economic Association, 24(4), 25-46.
  21. Oh, Seung-Won, Nam-Hui Im, Sang-Hyun Lee and Min-Soo Kim (2020), "Long-term Price Prediction and Trend Analysis of Garlic Using Prophet Model", Journal of The Korean Data Analysis Society, 22(6), 2325-2336. https://doi.org/10.37727/jkdas.2020.22.6.2325
  22. Park Da-In and Dae-Ha Kim (2019), "Long-term Prediction of Temperature in Korea Using Prophet Model", Proceedings of the Korean Meteorological Society Conference, 449.
  23. Rakholia, R., Q. Le, K. Vu, B. Q. Ho and R. S. Carbajo (2022), "AI-based air quality PM2.5 forecasting models for developing countries: A case study of Ho Chi Minh City, Vietnam", Urban Climate, 46, 101315. https://doi.org/10.1016/j.uclim.2022.101315
  24. Samal, K. K. R., K. S. Babu, S. K. Das and A. Acharaya (2019, August). "Time series based air pollution forecasting using SARIMA and prophet model", Proceedings of the 2019 International Conference on Information Technology and Computer Communications, 80-85. https://doi.org/10.1145/3355402.3355417
  25. Sardar, I., M. A. Akbar, V. Leiva, A. Alsanad and P. Mishra (2022), "Machine learning and automatic ARIMA/Prophet models-based forecasting of COVID-19: methodology, evaluation, and case study in SAARC countries", Stochastic Environmental Research and Risk Assessment, 1-15. https://doi.org/10.1007/s00477-022-02307-x
  26. Satrio, C. B. A., W. Darmawan, B. U. Nadia and N. Hanafiah (2021), "Time series analysis and forecasting of coronavirus disease in Indonesia using ARIMA model and PROPHET", Procedia Computer Science, 179, 524-532. https://doi.org/10.1016/j.procs.2021.01.036
  27. Shohan, M. J. A., M. O. Faruque and S. Y. Foo (2022), "Forecasting of Electric Load Using a Hybrid LSTM-Neural Prophet Model", Energies, 15(6), 2158. https://doi.org/10.3390/en15062158
  28. Taylor, S. J. and B. Letham (2018), "Forecasting at scale", The American Statistician, 72(1), 37-45. https://doi.org/10.1080/00031305.2017.1380080
  29. Toharudin, T., R. S. Pontoh, R. E. Caraka, S. Zahroh, Y. Lee and R. C. Chen (2020), "Employing long short-term memory and Facebook prophet model in air temperature forecasting", Communications in Statistics-Simulation and Computation, 1-24. https://doi.org/10.1080/03610918.2020.1854302
  30. Triebe, O., H. Hewamalage, P. Pilyugina, N. Laptev, C. Bergmeir and R. Rajagopal (2021), "Neuralprophet: Explainable forecasting at scale", arXiv preprint arXiv:2111.15397. https://doi.org/10.48550/arXiv.2111.15397
  31. Valipour, M. (2015), "Long-term runoff study using SARIMA and ARIMA models in the United States", Meteorological Applications, 22(3), 592-598. https://doi.org/10.1002/met.1491
  32. Zunic, E., K. Korjenic, K. Hodzic and D. Donko (2020), "Application of Facebook's Prophet Algorithm for Successful Sales Forecasting Based on Real-World Data", arXiv preprint arXiv:2005.07575. https://doi.org/10.48550/arXiv.2005.07575