Research Article
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Forecasting Shanghai Containerized Freight Index by Using Time Series Models

Year 2021, Volume: 10 Issue: 4, 426 - 434, 29.12.2021
https://doi.org/10.33714/masteb.1024663

Abstract

Recently, the container shipping industry has become unpredictable due to volatility and major events affecting the maritime sector. At the same time, approaches to estimating container freight rates using econometric and time series modelling have become very important. Therefore, in this paper, different time-series models have been explored that are related to the Shanghai Containerized Freight Index (SCFI). SMA, EWMA, and, SES, Holt Winter method are used to describe the data and model. Afterward, the Holt Winter method and SARIMA was applied to model and predict the SCFI index. MAPE, RMSE, AIC, BIC are used to measure the performances of the models and predictions. We observe that the SARIMA model provides comparatively better results than the existing freight rate forecasting models while performing short-term forecasts on a monthly rate. Results demonstrate that the increase will continue without losing momentum.

Thanks

This study was produced from the doctoral dissertation of the first author.

References

  • Agrawal, R. K., Muchahary, F., & Tripathi, M. M. (2018). Long term load forecasting with hourly predictions based on long-short-term-memory networks. 2018 IEEE Texas Power and Energy Conference (TPEC), 1-6. https://doi.org/10.1109/TPEC.2018.8312088
  • Awal, M., & Siddique, M. (2011). Rice production in Bangladesh employing by ARIMA model. Bangladesh Journal of Agricultural Research, 36(1), 51-62. https://doi.org/10.3329/bjar.v36i1.9229
  • BBC. (2021). Ever Given: Ship that blocked Suez Canal sets sail after deal signed. Retrieved on September 21, 2021, from https://www.bbc.com/news/world-middle-east-57746424
  • Burnham, K. P., & Anderson, D.R. (Eds.), (2004). Model Selection and Multimodel Inference. Springer.
  • Chen, P., Niu, A., Liu, D., Jiang, W., & Ma. B. (2018). Time series forecasting of temperatures using SARIMA: An example from Nanjing. IOP Conference Series: Materials Science and Engineering, 394(5), 052024. https://doi.org/10.1088/1757-899x/394/5/052024
  • Chou, M. -T. (2017). An improved fuzzy time series theory with applications in the Shanghai containerized freight index. Journal of Marine Science and Technology, 25(4), 393-398. https://doi.org/10.6119/JMST-017-0313-1
  • Dasyam, R., Pal, S., Rao, V. S., & Bhattacharyya, B. (2015). Time series modeling for trend analysis and forecasting wheat production of India. International Journal of Agriculture, Environment and Biotechnology, 8(2), 303-308. https://doi.org/10.5958/2230-732X.2015.00037.6
  • Dickey, D. A., & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association, 74(366a), 427-431. https://doi.org/10.1080/01621459.1979.10482531
  • Dixon, M. (2010). Hedging your bets. Containerisation International Year Book., National Magazine Company.
  • Drewry. (2021). COVID-19 to stifle container port investment. Retrieved on September 21, 2021, from https://www.drewry.co.uk/news/covid-19-to-stifle-container-port-investment
  • Dunya. (2021). Retrieved on September 21, 2021, from https://www.dunya.com/sektorler/lojistik/noel-sparisleri-one-cekildi-navlunda-yeni-zam-kapida-haberi-629042
  • Farhan, J., & Ong, G. P. (2018). Forecasting seasonal container throughput at international ports using SARIMA models. Maritime Economics & Logistics, 20, 131-148. https://doi.org/10.1057/mel.2016.13
  • Goulielmos, A. M., & Psifia, M.-E. (2009). Forecasting weekly freight rates for one-year time charter 65 000 dwt bulk carrier, 1989–2008, using nonlinear methods. Maritime Policy & Management, 36(5), 411-436. https://doi.org/10.1080/03088830903187150
  • Khogali, K. A., Odhiambo, J. W., & Owino. J. O. (2002). On smoothing time series data using a classical moving average formula. Interstat: Statistics on the Internet, Retrieved on September 21, 2021, from https://www.researchgate.net/profile/John-Odhiambo-3/publication/241913389_ON_SMOOTHING_TIME_SERIES_DATA_USING_A_CLASSICAL_MOVING_AVERAGE_FORMULA/links/58f78156aca2726498870d23/ON-SMOOTHING-TIME-SERIES-DATA-USING-A-CLASSICAL-MOVING-AVERAGE-FORMULA.pdf
  • Klein, A., & Verbeke. A. (1987). The design of an optimal short term forecasting system for seaport management: an application to the port of Antwerp. International Journal of Transport Economics/Rivista internazionale di economia dei trasporti, 14(1), 57-70.
  • Lucas, J. M., & Saccucci, M. S. (1990). Exponentially weighted moving average control schemes: Properties and enhancements. Technometrics, 32(1), 1-12. https://doi.org/10.1080/00401706.1990.10484583
  • Munim, Z. H., & Schramm, H. -J. (2017). Forecasting container shipping freight rates for the Far East–Northern Europe trade lane. Maritime Economics & Logistics, 19, 106-125. https://doi.org/10.1057/s41278-016-0051-7
  • Pang, G., & Gebka, B. (2017). Forecasting container throughput using aggregate or terminal-specific data? The case of Tanjung Priok Port, Indonesia. International Journal of Production Research, 55(9), 2454-2469. https://doi.org/10.1080/00207543.2016.1227102
  • Shanghai Shipping Exchange. (2020). Shanghai Export Containerized Freight Index (SCFI). Retrieved on September 21, 2021, from http://en.sse.net.cn/
  • Ship Technology. (2021). Global shipping container shortage: the story so far. Retrieved on September 21, 2021, from https://www.ship-technology.com/features/global-shipping-container-shortage-the-story-so-far/
  • Shu, M. H., Nguyen, T. L., Hsu, B., Lu, C., & Huang, J. -C. (2014). Forecasting cargo throughput with modified seasonal ARIMA models. WSEAS Transactions on Mathematics, 13, 171-181.
  • Stopford, M. (2009). Maritime Economics (3rd ed.). Routledge.
  • Yang, Y., Zheng, H., & Zhang, R. (2017). Prediction and analysis of aircraft failure rate based on SARIMA model. 2nd IEEE International Conference on Computational Intelligence and Applications (ICCIA), 2017, 567-571, https://doi.org/10.1109/CIAPP.2017.8167281
Year 2021, Volume: 10 Issue: 4, 426 - 434, 29.12.2021
https://doi.org/10.33714/masteb.1024663

Abstract

References

  • Agrawal, R. K., Muchahary, F., & Tripathi, M. M. (2018). Long term load forecasting with hourly predictions based on long-short-term-memory networks. 2018 IEEE Texas Power and Energy Conference (TPEC), 1-6. https://doi.org/10.1109/TPEC.2018.8312088
  • Awal, M., & Siddique, M. (2011). Rice production in Bangladesh employing by ARIMA model. Bangladesh Journal of Agricultural Research, 36(1), 51-62. https://doi.org/10.3329/bjar.v36i1.9229
  • BBC. (2021). Ever Given: Ship that blocked Suez Canal sets sail after deal signed. Retrieved on September 21, 2021, from https://www.bbc.com/news/world-middle-east-57746424
  • Burnham, K. P., & Anderson, D.R. (Eds.), (2004). Model Selection and Multimodel Inference. Springer.
  • Chen, P., Niu, A., Liu, D., Jiang, W., & Ma. B. (2018). Time series forecasting of temperatures using SARIMA: An example from Nanjing. IOP Conference Series: Materials Science and Engineering, 394(5), 052024. https://doi.org/10.1088/1757-899x/394/5/052024
  • Chou, M. -T. (2017). An improved fuzzy time series theory with applications in the Shanghai containerized freight index. Journal of Marine Science and Technology, 25(4), 393-398. https://doi.org/10.6119/JMST-017-0313-1
  • Dasyam, R., Pal, S., Rao, V. S., & Bhattacharyya, B. (2015). Time series modeling for trend analysis and forecasting wheat production of India. International Journal of Agriculture, Environment and Biotechnology, 8(2), 303-308. https://doi.org/10.5958/2230-732X.2015.00037.6
  • Dickey, D. A., & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association, 74(366a), 427-431. https://doi.org/10.1080/01621459.1979.10482531
  • Dixon, M. (2010). Hedging your bets. Containerisation International Year Book., National Magazine Company.
  • Drewry. (2021). COVID-19 to stifle container port investment. Retrieved on September 21, 2021, from https://www.drewry.co.uk/news/covid-19-to-stifle-container-port-investment
  • Dunya. (2021). Retrieved on September 21, 2021, from https://www.dunya.com/sektorler/lojistik/noel-sparisleri-one-cekildi-navlunda-yeni-zam-kapida-haberi-629042
  • Farhan, J., & Ong, G. P. (2018). Forecasting seasonal container throughput at international ports using SARIMA models. Maritime Economics & Logistics, 20, 131-148. https://doi.org/10.1057/mel.2016.13
  • Goulielmos, A. M., & Psifia, M.-E. (2009). Forecasting weekly freight rates for one-year time charter 65 000 dwt bulk carrier, 1989–2008, using nonlinear methods. Maritime Policy & Management, 36(5), 411-436. https://doi.org/10.1080/03088830903187150
  • Khogali, K. A., Odhiambo, J. W., & Owino. J. O. (2002). On smoothing time series data using a classical moving average formula. Interstat: Statistics on the Internet, Retrieved on September 21, 2021, from https://www.researchgate.net/profile/John-Odhiambo-3/publication/241913389_ON_SMOOTHING_TIME_SERIES_DATA_USING_A_CLASSICAL_MOVING_AVERAGE_FORMULA/links/58f78156aca2726498870d23/ON-SMOOTHING-TIME-SERIES-DATA-USING-A-CLASSICAL-MOVING-AVERAGE-FORMULA.pdf
  • Klein, A., & Verbeke. A. (1987). The design of an optimal short term forecasting system for seaport management: an application to the port of Antwerp. International Journal of Transport Economics/Rivista internazionale di economia dei trasporti, 14(1), 57-70.
  • Lucas, J. M., & Saccucci, M. S. (1990). Exponentially weighted moving average control schemes: Properties and enhancements. Technometrics, 32(1), 1-12. https://doi.org/10.1080/00401706.1990.10484583
  • Munim, Z. H., & Schramm, H. -J. (2017). Forecasting container shipping freight rates for the Far East–Northern Europe trade lane. Maritime Economics & Logistics, 19, 106-125. https://doi.org/10.1057/s41278-016-0051-7
  • Pang, G., & Gebka, B. (2017). Forecasting container throughput using aggregate or terminal-specific data? The case of Tanjung Priok Port, Indonesia. International Journal of Production Research, 55(9), 2454-2469. https://doi.org/10.1080/00207543.2016.1227102
  • Shanghai Shipping Exchange. (2020). Shanghai Export Containerized Freight Index (SCFI). Retrieved on September 21, 2021, from http://en.sse.net.cn/
  • Ship Technology. (2021). Global shipping container shortage: the story so far. Retrieved on September 21, 2021, from https://www.ship-technology.com/features/global-shipping-container-shortage-the-story-so-far/
  • Shu, M. H., Nguyen, T. L., Hsu, B., Lu, C., & Huang, J. -C. (2014). Forecasting cargo throughput with modified seasonal ARIMA models. WSEAS Transactions on Mathematics, 13, 171-181.
  • Stopford, M. (2009). Maritime Economics (3rd ed.). Routledge.
  • Yang, Y., Zheng, H., & Zhang, R. (2017). Prediction and analysis of aircraft failure rate based on SARIMA model. 2nd IEEE International Conference on Computational Intelligence and Applications (ICCIA), 2017, 567-571, https://doi.org/10.1109/CIAPP.2017.8167281
There are 23 citations in total.

Details

Primary Language English
Subjects Maritime Engineering (Other)
Journal Section Research Article
Authors

Kaan Koyuncu 0000-0001-7984-692X

Leyla Tavacıoğlu 0000-0002-9919-8226

Publication Date December 29, 2021
Submission Date November 16, 2021
Acceptance Date December 23, 2021
Published in Issue Year 2021 Volume: 10 Issue: 4

Cite

APA Koyuncu, K., & Tavacıoğlu, L. (2021). Forecasting Shanghai Containerized Freight Index by Using Time Series Models. Marine Science and Technology Bulletin, 10(4), 426-434. https://doi.org/10.33714/masteb.1024663
AMA Koyuncu K, Tavacıoğlu L. Forecasting Shanghai Containerized Freight Index by Using Time Series Models. Mar. Sci. Tech. Bull. December 2021;10(4):426-434. doi:10.33714/masteb.1024663
Chicago Koyuncu, Kaan, and Leyla Tavacıoğlu. “Forecasting Shanghai Containerized Freight Index by Using Time Series Models”. Marine Science and Technology Bulletin 10, no. 4 (December 2021): 426-34. https://doi.org/10.33714/masteb.1024663.
EndNote Koyuncu K, Tavacıoğlu L (December 1, 2021) Forecasting Shanghai Containerized Freight Index by Using Time Series Models. Marine Science and Technology Bulletin 10 4 426–434.
IEEE K. Koyuncu and L. Tavacıoğlu, “Forecasting Shanghai Containerized Freight Index by Using Time Series Models”, Mar. Sci. Tech. Bull., vol. 10, no. 4, pp. 426–434, 2021, doi: 10.33714/masteb.1024663.
ISNAD Koyuncu, Kaan - Tavacıoğlu, Leyla. “Forecasting Shanghai Containerized Freight Index by Using Time Series Models”. Marine Science and Technology Bulletin 10/4 (December 2021), 426-434. https://doi.org/10.33714/masteb.1024663.
JAMA Koyuncu K, Tavacıoğlu L. Forecasting Shanghai Containerized Freight Index by Using Time Series Models. Mar. Sci. Tech. Bull. 2021;10:426–434.
MLA Koyuncu, Kaan and Leyla Tavacıoğlu. “Forecasting Shanghai Containerized Freight Index by Using Time Series Models”. Marine Science and Technology Bulletin, vol. 10, no. 4, 2021, pp. 426-34, doi:10.33714/masteb.1024663.
Vancouver Koyuncu K, Tavacıoğlu L. Forecasting Shanghai Containerized Freight Index by Using Time Series Models. Mar. Sci. Tech. Bull. 2021;10(4):426-34.

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