Forecasting Baltic Dirty Tanker Index by Applying Wavelet Neural Networks

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Journal of Transportation Technologies

ISSN Print: 2160-0473
ISSN Online: 2160-0481
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"Forecasting Baltic Dirty Tanker Index by Applying Wavelet Neural Networks"
written by Shuangrui Fan, Tingyun Ji, Wilmsmeier Gordon, Bergqvist Rickard,
published by Journal of Transportation Technologies, Vol.3 No.1, 2013

has been cited by the following article(s):

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[13]	An empirical study on Forecasting Crude Oil Tanker Freight Rates and the impact of exogenous variables
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[14]	Optimization of Artificial Neural Network for Forecasting Earning of Dirty Tanker Markets
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[15]	基于 VMD-FFT-LSTM 模型的 BDI 指数预测
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[16]	Evaluation of Forecasting Approaches for Crude Oil Tanker Freight Rates
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[17]	IMPROVED ENSEMBLE AVERAGE APPROACH FOR SYSTOLIC BLOOD PRESSURE ESTIMATION
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[18]	Econometric analyses of liquefied petroleum gas and product tanker shipping markets
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[19]	Development of Machine Learning Methods to Improve Decisions in Bulk Chartering Practice
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[20]	Developing a framework for forecasting financial time series using Wavelet-LSTM hybrid model
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[21]	Predikcija vremenskih nizova pomoću talasića i neuralnih mreža sa primenom za predikciju valutnih parova
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[23]	Reduktion der time-to-market und des Modellrisikos mit einem informations-basierten Architekturmodell
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[24]	Supramax Bulk Carrier Market Forecasting with Technical Indicators and Neural Networks
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[25]	Predicting Shipping Freight Rate Movements Using Recurrent Neural Networks and AIS Data-On the tanker route between the Arabian Gulf and Singapore
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[26]	Reduktion der time-to-market und des Modellrisikos mit einem informationsbasierten Architekturmodell
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[27]	Real time traffic control system using wavelet neural networks signal for wireless communication technology of future high speed railways performance in Thailand …
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[28]	Stimulus based algorithm for extracting electrocardiogram signal from the head for eyewear devices
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[29]	The value of options for time charterparty extension: an artificial neural networks (ANN) approach
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[30]	Investigation of the Relations between Crude Oil Prices and Real Effective Exchange Rate (REER) in Iran using Phase-Angle Analysis
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[31]	평균-분산 최적화 모형을 이용한 로버스트 선박운항 일정계획
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[32]	Financial Time Series Forecasting Using Hybrid Wavelet-Neural Model
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[33]	A Robust Ship Scheduling Based on Mean-Variance Optimization Model
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[34]	EUR/RSD exchange rate forecasting using hybrid wavelet-neural model: A case study
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[35]	Tool health monitoring for wood milling process using Airborne Acoustic Emission
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[36]	Tool health monitoring for wood milling process using airborne acoustic emission.
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[37]	평균-분산 최적화 모형을 이용한 로버스트 선박운항일정계획
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[1]	Valuation of the Extension Option in Time Charter Contracts in the LNG Market Energies, 2022 DOI:10.3390/en15186737

[2]	An investigation on behavioral biases in ship investments of small-sized shipping companies Maritime Business Review, 2021 DOI:10.1108/MABR-08-2020-0049

[3]	Support Vector Machine Algorithms: An Application to Ship Price Forecasting Computational Economics, 2020 DOI:10.1007/s10614-020-10032-2

[4]	Analysing and forecasting China containerized freight index with a hybrid decomposition–ensemble method based on EMD, grey wave and ARMA Grey Systems: Theory and Application, 2020 DOI:10.1108/GS-05-2020-0069

[5]	Capesize markets behavior: Explaining volatility and expectations The Asian Journal of Shipping and Logistics, 2020 DOI:10.1016/j.ajsl.2020.08.001

[6]	Forecasting Tanker Indices with ARIMA-SVM Hybrid Models Korean Journal of Financial Engineering, 2018 DOI:10.35527/kfedoi.2018.17.4.004

[7]	The value of options for time charterparty extension: an artificial neural networks (ANN) approach Maritime Policy & Management, 2017 DOI:10.1080/03088839.2017.1392630

[8]	A Robust Ship Scheduling Based on Mean-Variance Optimization Model Journal of the Korean Operations Research and Management Science Society, 2016 DOI:10.7737/JKORMS.2016.41.2.129

[9]	Tool health monitoring for wood milling process using airborne acoustic emission 2015 IEEE International Conference on Automation Science and Engineering (CASE), 2015 DOI:10.1109/CoASE.2015.7294315

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	customer@scirp.org
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