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Forecasting Baltic ...
Forecasting Baltic Dirty Tanker Index by Applying Wavelet Neural Networks
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- Fan, Shuangrui (author)
- Gothenburg University,Göteborgs universitet,Företagsekonomiska institutionen, Industriell och Finansiell ekonomi & logistik,Department of Business Administration, Industrial and Financial Management & Logistics
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- Tingyun, Ji (author)
- Gothenburg University,Göteborgs universitet,Företagsekonomiska institutionen, Industriell och Finansiell ekonomi & logistik,Department of Business Administration, Industrial and Financial Management & Logistics
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Wilmsmeier, Gordon (author)
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- Bergqvist, Rickard, 1979 (author)
- Gothenburg University,Göteborgs universitet,Företagsekonomiska institutionen, Industriell och Finansiell ekonomi & logistik,Department of Business Administration, Industrial and Financial Management & Logistics
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(creator_code:org_t)
- Scientific Research Publishing, Inc. 2013
- 2013
- English.
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In: Journal of Transportation Technologies. - : Scientific Research Publishing, Inc.. - 2160-0473 .- 2160-0481. ; 3:1, s. 68-87
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https://gup.ub.gu.se... (primary) (free)
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http://www.scirp.org...
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https://doi.org/10.4...
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Abstract
Subject headings
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- Baltic Exchange Dirty Tanker Index (BDTI) is an important assessment index in world dirty tanker shipping industry. Actors in the industry sector can gain numerous benefits from accurate forecasting of the BDTI. However, limitations exist in traditional stochastic and econometric explanation modeling techniques used in freight rate forecasting. At the same time research in shipping index forecasting e.g. BDTI applying artificial intelligent techniques is scarce. This analyses the possibilities to forecast the BDTI by applying Wavelet Neural Networks (WNN). Firstly, the characteristics of traditional and artificial intelligent forecasting techniques are discussed and rationales for choosing WNN are explained. Secondly, the components and features of BDTI will be explicated. After that, the authors delve the determinants and influencing factors behind fluctuations of the BDTI in order to set inputs for WNN forecasting model. The paper examines non-linearity and non-stationary features of the BDTI and elaborates WNN model building procedures. Finally, the comparison of forecasting performance between WNN and ARIMA time series models show that WNN has better forecasting accuracy than traditionally used modeling techniques.
Subject headings
- SAMHÄLLSVETENSKAP -- Ekonomi och näringsliv -- Företagsekonomi (hsv//swe)
- SOCIAL SCIENCES -- Economics and Business -- Business Administration (hsv//eng)
Keyword
- BDTI
- tanker freight rates
- forecasting
- wavelets
- neural networks
- shipping finance
Publication and Content Type
- ref (subject category)
- art (subject category)
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