# Forecasting Liquefied Natural Gas Bunker Prices Using Artificial Neural Network for Procurement Management

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## Abstract

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## 1. Introduction

#### 1.1. Background

#### 1.2. Purpose

#### 1.3. Literature Review

## 2. Materials and Methods

#### 2.1. Data

#### 2.2. Research Modeling

#### 2.2.1. Simple RNN

#### 2.2.2. LSTM

#### 2.2.3. GRU

#### 2.2.4. Hyper Parameter Modelling

#### 2.3. Performance Indicators

## 3. Results

## 4. Discussion

## 5. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

- Wang, S.; Meng, Q.; Liu, Z. Bunker consumption optimization methods in shipping: A critical review and extensions. Transp. Res. Part E Logist. Transp. Rev.
**2013**, 53, 49–62. [Google Scholar] [CrossRef] - Stopford, M. Maritime Economics 3e; Routledge: London, UK, 2009. [Google Scholar]
- Notteboom, T.E.; Vernimmen, B. The effect of high fuel costs on liner service configuration in container shipping. J. Transp. Geogr.
**2009**, 17, 325–337. [Google Scholar] [CrossRef] - Alizadeh, A.H.; Nomikos, N.K. Shipping Derivatives and Risk Management; Palgrave Macmillan: London, UK, 2009; ISBN 978-0-230-21591-7. [Google Scholar]
- Clarkson Research Shipping Intelligence Network. Available online: https://sin.clarksons.net/ (accessed on 19 March 2022).
- Platts bunkerwire. Available online: https://www.spglobal.com/commodityinsights/en/products-services/shipping/bunkerwire (accessed on 28 September 2022).
- Available online: https://www.jpmorgan.com/insights/research/oil-gas-energy-prices (accessed on 28 September 2022).
- Ronen, D. The effect of oil price on containership speed and fleet size. J. Oper. Res. Soc.
**2011**, 62, 211–216. [Google Scholar] [CrossRef] - Alizadeh, A.H.; Kavussanos, M.G.; Menachof, D.A. Hedging against bunker price fluctuations using petroleum futures contracts: Constant versus time-varying hedge ratios. Appl. Econ.
**2004**, 36, 1337–1353. [Google Scholar] [CrossRef] - Qi, J.; Wang, H.; Zheng, J. Promoting liquefied natural gas (LNG) bunkering for maritime transportation: Should ports or ships be subsidized? Sustainability
**2022**, 14, 6647. [Google Scholar] [CrossRef] - Livaniou, S.; Chatzistelios, G.; Lyridis, D.V.; Bellos, E. LNG vs. MDO in marine fuel emissions tracking. Sustainability
**2022**, 14, 3860. [Google Scholar] [CrossRef] - Lloyd’s Register Advisory Service Korea LNG Bunkering Review (Busan, Incheon, and Ulsan Ports). 2022.
- Stefanakos, C.N.; Schinas, O. Forecasting bunker prices; A nonstationary, multivariate methodology. Transp. Res. Part C Emerg. Technol.
**2014**, 38, 177–194. [Google Scholar] [CrossRef] - Stefanakos, C.; Schinas, O. Fuzzy time series forecasting of bunker prices: Nonstationary considerations. WMU J. Marit. Aff.
**2015**, 14, 177–199. [Google Scholar] [CrossRef] - Choi, J. Forecasting Bunker Price Using System Dynamics. J. Korea Port Econ. Assoc.
**2017**, 33, 75–87. [Google Scholar] [CrossRef] - Kim, K. A Study on the Forecasting of Bunker Price Using Recurrent Neural Network. J. Korea Soc. Comput. Inf.
**2021**, 26, 179–184. [Google Scholar] [CrossRef] - Yu, L.; Wang, S.; Lai, K.K. Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm. Energy Econ.
**2008**, 30, 2623–2635. [Google Scholar] [CrossRef] - Yu, L.; Zhang, X.; Wang, S. Assessing potentiality of support vector machine method in crude oil price forecasting. Eurasia J. Math. Sci. Technol. Educ.
**2017**, 13, 7893–7904. [Google Scholar] [CrossRef] - Jammazi, R.; Aloui, C. Crude oil price forecasting: Experimental evidence from wavelet decomposition and neural network modeling. Energy Econ.
**2012**, 34, 828–841. [Google Scholar] [CrossRef] - Salvi, H. Long short-term model for brent oil price forecasting. Int. J. Res. Appl. Sci. Eng. Technol.
**2019**, 7, 315–319. [Google Scholar] [CrossRef] - Güleryüz, D.; Özden, E. The prediction of Brent crude oil trend using LSTM and Facebook prophet. Avrupa Bilim Ve Teknol. Derg.
**2020**, 1–9. [Google Scholar] [CrossRef] - Wu, Y.; Wu, Q.; Zhu, J. Improved EEMD-based crude oil price forecasting using LSTM networks. Physica A
**2019**, 516, 114–124. [Google Scholar] [CrossRef] - Manowska, A.; Bluszcz, A. Forecasting Crude Oil Consumption in Poland Based on LSTM Recurrent Neural Network. Energies
**2022**, 15, 4885. [Google Scholar] [CrossRef] - Rumelhart, D.E.; Hinton, G.E.; Williams, R.J. Learning representations by back-propagating errors. Nature
**1986**, 323, 533–536. [Google Scholar] [CrossRef] - Hochreiter, S.; Schmidhuber, J. Long short-term memory. Neural Comput.
**1997**, 9, 1735–1780. [Google Scholar] [CrossRef] - Cho, K.; van Merrienboer, B.; Gulcehre, C.; Bahdanau, D.; Bougares, F.; Schwenk, H.; Bengio, Y. Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation. 2014. Available online: https://arxiv.org/abs/1406.1078 (accessed on 28 September 2022).
- Kim, D.; Kim, H.; Sim, S.; Choi, Y.; Bae, H.; Yun, H. Prediction of dry bulk freight index using deep learning. J. Korean Inst. Ind. Eng.
**2019**, 45, 111–116. [Google Scholar] [CrossRef] - Han, M.; Yu, S. Prediction of Baltic Dry Index by Applications of Long Short-Term Memory. J. Korean Soc. Qual. Manag.
**2019**, 47, 497–508. [Google Scholar] - Lim, S.; Yun, H. Forecasting bulk market indices with recurrent neural network models. J. Marit. Bus.
**2018**, 40, 159–180. [Google Scholar] - Cybenko, G. Approximation by superpositions of a sigmoidal function. Math. Control Signals Syst.
**1989**, 2, 303–314. [Google Scholar] [CrossRef] - Zhang, G.; Patuwo, B.E.; Hu, M.Y. Forecasting with artificial neural networks: The state of the art. Int. J. Forecast.
**1998**, 14, 35–62. [Google Scholar] [CrossRef] - Willmott, C.J.; Matsuura, K. Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Clim. Res.
**2005**, 30, 79–82. [Google Scholar] [CrossRef] - Wallach, D.; Goffinet, B. Mean squared error of prediction as a criterion for evaluating and comparing system models. Ecol. Model.
**1989**, 44, 299–306. [Google Scholar] [CrossRef] - Chai, T.; Draxler, R.R. Root mean square error (RMSE) or mean absolute error (MAE)?—Arguments against avoiding RMSE in the literature. Geosci. Model Dev.
**2014**, 7, 1247–1250. [Google Scholar] [CrossRef] - de Myttenaere, A.; Golden, B.; Le Grand, B.; Rossi, F. Mean absolute percentage error for regression models. Neurocomputing
**2016**, 192, 38–48. [Google Scholar] [CrossRef] - Diebold, F.X.; Mariano, R.S. Comparing predictive accuracy. J. Bus. Econ. Stat.
**2002**, 20, 134–144. [Google Scholar] [CrossRef] - Lin, H.; Sun, Q. Crude oil prices forecasting: An approach of using ceemdan-based multi-layer gated recurrent unit networks. Energies
**2020**, 13, 1543. [Google Scholar] [CrossRef]

**Figure 1.**LNG-capable orderbook trend (adapted from Clarkson research [5]). LNG, liquefied natural gas.

**Figure 2.**LNG bunker price trend in the port of Singapore (computed by the authors using data from Platts Bunkerwire [6]). LNG, liquefied natural gas; VLSFO, very low sulfur fuel oil.

**Figure 4.**Prediction of LNG bunker price by each method. RNN, recurrent neural network; LSTM, long short-term memory; GRU, gated recurrent unit.

Period | Prediction |
---|---|

Early 2020s | Gradual ramp up of deliveries of LNG-fueled ships |

2024–2030 | LNG-fueled ship deliveries begin to surpass those of conventional oil-fueled ships |

2030s | LNG-fueled ships shares begin to fall as zero-carbon technologies develop |

2040s | Zero-carbon vessels account for the major share of shipyard output |

Statistics | Weekly LNG Bunker Price | |
---|---|---|

Observations | 144 | |

Mean | 15.04 | |

Std. error | 0.83 | |

Median | 11.09 | |

Std. dev. | 10.00 | |

ADF test | t-stat. | −1.85 |

Prob | 0.353 |

Model | MAE | MSE | MAPE | RMSE | |
---|---|---|---|---|---|

Simple RNN | Tr | 1.17 | 6 | 11.81 | 2.45 |

Te | 4.26 | 38.13 | 14.14 | 6.18 | |

LSTM | Tr | 1.23 | 5.75 | 12.19 | 2.4 |

Te | 4.14 | 33.81 | 13.77 | 5.82 | |

GRU | Tr | 1.15 | 5.26 | 12.1 | 2.29 |

Te | 5.09 | 47.52 | 16.81 | 6.89 |

Benchmark | Squared Error | Absolute Error | Squared Proportional Error | |||
---|---|---|---|---|---|---|

Simple RNN | GRU | Simple RNN | GRU | Simple RNN | GRU | |

LSTM | −1.254 (0.105) | −2.623 (0.004) | −0.577 (0.282) | −3.159 (0.001) | −1.029 (0.152) | −3.289 (0.001) |

Simple RNN | −1.576 (0.058) | −2.160 (0.015) | −1.702 (0.044) |

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## Share and Cite

**MDPI and ACS Style**

Kim, K.; Lim, S.; Lee, C.-h.; Lee, W.-J.; Jeon, H.; Jung, J.; Jung, D. Forecasting Liquefied Natural Gas Bunker Prices Using Artificial Neural Network for Procurement Management. *J. Mar. Sci. Eng.* **2022**, *10*, 1814.
https://doi.org/10.3390/jmse10121814

**AMA Style**

Kim K, Lim S, Lee C-h, Lee W-J, Jeon H, Jung J, Jung D. Forecasting Liquefied Natural Gas Bunker Prices Using Artificial Neural Network for Procurement Management. *Journal of Marine Science and Engineering*. 2022; 10(12):1814.
https://doi.org/10.3390/jmse10121814

**Chicago/Turabian Style**

Kim, Kyunghwan, Sangseop Lim, Chang-hee Lee, Won-Ju Lee, Hyeonmin Jeon, Jinwon Jung, and Dongho Jung. 2022. "Forecasting Liquefied Natural Gas Bunker Prices Using Artificial Neural Network for Procurement Management" *Journal of Marine Science and Engineering* 10, no. 12: 1814.
https://doi.org/10.3390/jmse10121814