Forecasting the Baltic Dry Index by using an artificial neural network approach

doi:10.3906/elk-1706-155

Cited by 11 publications

(5 citation statements)

References 16 publications

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“…The generalization ability of ANN provides conditions for its potential in prediction. ANN cannot only be applied in shipping economy market [13,15,21,31,32,40], but also provide help for ship technological design. Study in [5], respectively, uses ANN to estimate engine power and fuel consumption, and then estimate carbon dioxide emissions, for the recent tankers, bulk carriers and container ships built from 2015 to present.…”

Section: Literature Reviewmentioning

confidence: 99%

Forecasting Secondhand Tanker Price Through Wavelet Neural Networks Based on Adaptive Genetic Algorithm

2023

ITC

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Seaborne crude oil remains the main source of energy in the modern world in terms of volume, accounting for nearly half of all internationally traded crude oil. The shipping market is already characterized by high volatility, coupled with the impact of COVID-19 lockdown and geopolitics events. Price forecasting has become a necessary and challenging task for shipowners and other stakeholders. In the shipping market forecasting literature, the usual focus is on the newbuilding ship price or freight rate. A limited number of literature is for secondhand tanker price. On the other hand, there is few literature that use wavelet neural networks based on adaptive genetic algorithm (AGA-WNN) to predict shipping market. This paper mainly studies the application of the hybrid model to secondhand price prediction of 5 kinds of tanker sizes. The performance of AGA-WNN on time series of 10 and 15 years is compared with the basic performance provided by the six benchmark models, using three error metrics and two statistical tests. We can point out that AGA-WNN provides encouraging and promising results, outperforming the baseline models in both accuracy and robustness. It can be said that AGA-WNN gives the best overall predictive performance.

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Section: Literature Reviewmentioning

confidence: 99%

Forecasting Secondhand Tanker Price Through Wavelet Neural Networks Based on Adaptive Genetic Algorithm

2023

ITC

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“…Cho and Lin used a fuzzy neural network model to analyze and forecast BDI [5], and Kamal et al [6] forecast BDI as a high-dimensional multivariate regression problem by using deep neural networks. Sahin et al [7] predicted one-step-ahead BDI values by their proposed three artificial neural networks, specifically a univariate model and two bivariate models, by harnessing historical BDI data and the world price of crude oil. Qingcheng et al [8] proposed a decomposition technique for BDI data, and then used a neural network for prediction.…”

Section: Related Workmentioning

confidence: 99%

DERN: Deep Ensemble Learning Model for Short- and Long-Term Prediction of Baltic Dry Index

et al. 2020

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The Baltic Dry Index (BDI) is a commonly utilized indicator of global shipping and trade activity. It influences stakeholders’ and ship-owners’ decisions respecting investments, chartering, operational plans, and export and import activities. Accurate prediction of the BDI is very challenging due to its volatility, non-stationarity, and complexity. To help stakeholders and ship-owners make sound short- and long-term maritime business decisions and avoid market risk, we performed short- and long-term predictions of BDI using an ensemble deep-learning approach. In this study, we propose to apply recurrent neural network models for BDI prediction. The state-of-the-art of sequential deep-learning models such as RNN, LSTM, and GRU are employed to predict one- and multi-step-ahead BDI values. In order to increase the accuracy, we assemble the models. In experiments, we compared our results with those of traditional methods such as ARIMA and MLP. The results showed that our proposed method outperforms ARIMA, MLP, RNN, LSTM, and GRU in both short- and long-term prediction of BDI.

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“…Sahin et al [17] set out to develop an ANN approach to forecast the Baltic Dry Index, a shipping sector indicator of global economic activity. Using Baltic Dry Index data from 2010-2016, they developed three ANN models: one involving weekly data from the Baltic Dry Index, one on weekly data regarding crude oil prices, and a combination of the two.…”

Section: Introductionmentioning

confidence: 99%

Estimating bulk carriers’ main engine power and emissions

Güneş¹

2023

brod

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Great importance is had in understanding the current situation of maritime transport and making predictions about its future. Maritime transport is an essential part of transportation, and correctly predicting installed main engine power has great significance in maritime transport with regard to fuel consumption and the generation of emissions. Nonlinear regression is a method with great potential in making predictions, as it allows for more realistic models to be developed using multiple variables. Vessels' dimensions of carrying capacity, gross tonnage, length, and breadth significantly impact the required main engine power. This article will calculate and estimate the installed main engine power for bulk carriers through nonlinear regression using data for the as yet highest number of bulk carriers (n = 9,174 ships) and compare the results with the studies in the literature. The developed model has an accuracy of 93.2% for six different bulk carrier types (Small, Handysize, Handymax, Panamax, Capesize, and Large Capesize). In addition, the study calculates the emissions these ships produce (NOx, SO2, CO2, HC, PM), estimating and demonstrating a nonlinear linear regression model for these ships' emission amounts. The performed analyses have found the main engine power required per unit of load to decrease as ship size increases. However, these analyses also show the emissions generated per unit of load to decrease as size increases, with Large Capesize vessels being found to have the lowest fuel consumption and emission generation per unit of load.

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Forecasting the Baltic Dry Index by using an artificial neural network approach

Cited by 11 publications

References 16 publications

Forecasting Secondhand Tanker Price Through Wavelet Neural Networks Based on Adaptive Genetic Algorithm

Forecasting Secondhand Tanker Price Through Wavelet Neural Networks Based on Adaptive Genetic Algorithm

DERN: Deep Ensemble Learning Model for Short- and Long-Term Prediction of Baltic Dry Index

Estimating bulk carriers’ main engine power and emissions

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