عنوان مقاله
شاخص BDTI با بکارگیری شبکه های عصبی Wavelet
فهرست مطالب
مقدمه
مرور ادبیات
مدلسازی
مدلسازی نتایج
نتیجه گیری
بخشی از مقاله
بندرهای بارگیری و تخلیه: بندرهای بارگیری و یا تخلیه ممکن است در خارج از تعاریف مسیر بصورت تفاوت موجود بین بندرهایی که بر اساس اهمیت بازار ارزیابی می شوند معین شوند.به این ترتیب این تعریف شامل فاکتورهای هزینه های بندر زمان اضافی ، پس انداز ، و دیگر ارزش افزوده نیز بدلیل اختلافهای جغرافیایی نیز میگردد.
کلمات کلیدی:
Forecasting Baltic Dirty Tanker Index by Applying Wavelet Neural Networks Shuangrui Fan1 , Tingyun Ji1 , Wilmsmeier Gordon2,3, Bergqvist Rickard1 1 Logistics and Transport Research Group, Department of Business Administration, School of Business, Economics and Law at University of Gothenburg, Göteborg, Sweden 2 Economic Commission for Latin America and the Caribbean (ECLAC), Santiago, Chile 3 Transport Research Institute (TRI), Edinburgh Napier University, Edinburgh, UK Email: * gordon.wilmsmeier@cepal.org Received November 12, 2012; revised December 15, 2012; accepted December 25, 2012 ABSTRACT 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 an
