Newbuilding ship price forecasting by parsimonious intelligent model search engine

Gao, Ruobin; Liu, Jiahui; Zhou, Qin; Duru, Okan; Yuen, Kum Fai

doi:10.1016/j.eswa.2022.117119

Cited by 14 publications

(2 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [8,37], a multivariate vector autoregressive model (VARX) containing exogenous variables is established to improve the prediction accuracy of BDI. There are also other novel forecasting methods, for example, judgmental forecasting [14], copula-based multivariate models [42], fuzzy time series modelling approach [17] and popular machine learning algorithms [19,39]. Although it is convenient to use the linear or nonlinear methods mentioned above, there are certain restrictions on the number of dependent and independent variables.…”

Section: Literature Reviewmentioning

confidence: 99%

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

2023

ITC

View full text Add to dashboard Cite

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.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

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

2023

ITC

View full text Add to dashboard Cite

show abstract

“…For instance, Gao & Duru (2020) define the fitness function fo the GA as the performance on the validation set which locates at the end of the available series. The forecasting errors on the validation set are utilized to select the combination of time lags and variables by Gao et al (2022). The validation set also locates between the test and training sets, so it is feasible to optimize the length of training sets.…”

Section: Cross-validation For Time Seriesmentioning

confidence: 99%

Electricity load forecasting by randomized neural networks

Gao¹

Self Cite

View full text Add to dashboard Cite

I have been enjoying study and life at NTU during the past four years, including one year for my master's degree. I learned the most cutting-edge knowledge about developing algorithms, and met the most far-sighted friends. For a young researcher, nothing is dearer than studying and working at NTU. First of all, I would like to express my sincere gratitude to my first supervisor Dr. Okan Duru for the continuous support of my Ph.D. study and related research. After Dr. Okan Duru left Singapore, Dr.Yuen Kum Fai supervised me with his patience, motivation, and immense knowledge for the following years. Thanks for his encouragement and financial support after I submitted the thesis. At the same time, I would like to express my respect and gratitude to Dr. P.N. Suganthan, who co-supervised me unofficially but helpfully. He shared constructive ideas with me every two weeks.The colleagues, Jiahui Liu, and Liang Du, in the group, are amicable and helpful. I thank my fellow colleagues for the stimulating discussions during a group meeting, for the collaboration to produce novel ideas and papers, and for all the fun we have had in the last three years.Moreover, I acknowledge the people who mean a lot to me, my parents. Thanks to their strong support and infinite love, I have the opportunity to pursue higher degrees overseas. I wish to express my most incredible gratitude to my them.

show abstract

Automatic topology optimization of echo state network based on particle swarm optimization

Xue

Zhang

Słowik

2023

Engineering Applications of Artificial Intelligence

View full text Add to dashboard Cite

Newbuilding ship price forecasting by parsimonious intelligent model search engine

Cited by 14 publications

References 39 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

Electricity load forecasting by randomized neural networks

Automatic topology optimization of echo state network based on particle swarm optimization

Contact Info

Product

Resources

About