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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[17]	Ανάλυση και πρόβλεψη των τιμών ναύλων στην αγορά tanker. Πρόβλεψη της τάσης στην αγορά spot
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[3]	Valuation of the Extension Option in Time Charter Contracts in the LNG Market Energies, 2022 DOI:10.3390/en15186737

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

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

[6]	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

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

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

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

[10]	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

[11]	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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