A novel hybrid approach to Baltic Dry Index forecasting based on a combined dynamic fluctuation network and artificial intelligence method

[1]  F. Chen,et al.  Asymmetric volatility varies in different dry bulk freight rate markets under structure breaks , 2018, Physica A: Statistical Mechanics and its Applications.

[2]  Roy Batchelor,et al.  Forecasting spot and forward prices in the international freight market , 2007 .

[3]  Okan Duru,et al.  A fuzzy extended DELPHI method for adjustment of statistical time series prediction: An empirical study on dry bulk freight market case , 2012, Expert Syst. Appl..

[4]  N. Obuchowski,et al.  Assessing the Performance of Prediction Models: A Framework for Traditional and Novel Measures , 2010, Epidemiology.

[5]  Qiang Meng,et al.  Liner container seasonal shipping revenue management , 2015 .

[6]  S. Strogatz Nonlinear Dynamics and Chaos: With Applications to Physics, Biology, Chemistry and Engineering , 1995 .

[7]  Lixin Tian,et al.  Exact results of the limited penetrable horizontal visibility graph associated to random time series and its application , 2017, Scientific Reports.

[8]  Dimitris A. Tsouknidis,et al.  A survey of shipping finance research: Setting the future research agenda , 2018, Transportation Research Part E: Logistics and Transportation Review.

[9]  N. Nomikos,et al.  Shipping Investor Sentiment and International Stock Return Predictability , 2016 .

[10]  Nicholas Apergis,et al.  New Evidence on the Information and Predictive Content of the Baltic Dry Index , 2013 .

[11]  Ventsislav Nikolov,et al.  A wavelet and neural network model for the prediction of dry bulk shipping indices , 2012 .

[12]  F. Chen,et al.  The Scaling Behavior of Bulk Freight Rate Volatility , 2016 .

[13]  Daewon Lee,et al.  Predicting tanker freight rates using parsimonious variables and a hybrid artificial neural network with an adaptive genetic algorithm , 2017 .

[14]  M. P. Hanias,et al.  Forecasting Financial Indices: The Baltic Dry Indices , 2013 .

[15]  Umit Ilhan,et al.  Long Term Dry Cargo Freight Rates Forecasting by Using Recurrent Fuzzy Neural Networks , 2016 .

[16]  Michael G. Parsons,et al.  Forecasting tanker freight rate using neural networks , 1997 .

[17]  F. Dong,et al.  Forewarning of Freight Rate in Shipping Market Based on Support Vector Machine , 2008 .

[18]  Okan Duru,et al.  A fuzzy integrated logical forecasting (FILF) model of time charter rates in dry bulk shipping: A vector autoregressive design of fuzzy time series with fuzzy c-means clustering , 2012 .

[19]  A. Alizadeh,et al.  Stock market efficiency and international shipping-market information , 2014 .

[20]  Lixin Tian,et al.  A novel hybrid method of forecasting crude oil prices using complex network science and artificial intelligence algorithms , 2018, Applied Energy.

[21]  Zoubin Ghahramani,et al.  Probabilistic machine learning and artificial intelligence , 2015, Nature.

[22]  Alexandros M. Goulielmos,et al.  Forecasting weekly freight rates for one-year time charter 65 000 dwt bulk carrier, 1989–2008, using nonlinear methods , 2009 .

[23]  Nicholas Sim,et al.  Trade, income and the Baltic Dry Index , 2013 .

[24]  Michael Small,et al.  Complex network analysis of time series , 2016 .

[25]  P. Franses,et al.  A co-integration approach to forecasting freight rates in the dry bulk shipping sector , 1997 .

[26]  M. Small,et al.  Characterizing system dynamics with a weighted and directed network constructed from time series data. , 2014, Chaos.

[27]  Manolis G. Kavussanos,et al.  Seasonality patterns in dry bulk shipping spot and time charter freight rates , 2001 .

[28]  H. B. Barlow,et al.  Unsupervised Learning , 1989, Neural Computation.

[29]  Hilde Meersman,et al.  Forecasting spot rates at main routes in the dry bulk market , 2012 .

[30]  Lucas Lacasa,et al.  From time series to complex networks: The visibility graph , 2008, Proceedings of the National Academy of Sciences.

[31]  Kevin Cullinane,et al.  A Comparison of Models for Forecasting the Baltic Freight Index: Box-Jenkins Revisited , 1999 .

[32]  Stratos Papadimitriou,et al.  A Novel Approach to Forecasting the Bulk Freight Market , 2017 .

[33]  M. Luo,et al.  Examining the theoretical–empirical inconsistency on stationarity of shipping freight rate , 2018 .

[34]  Guobao Ning,et al.  Forecasting Dry Bulk Freight Index with Improved SVM , 2014 .

[35]  Işıl Şahin Onat,et al.  Baltic Dry Index as a Major Economic Policy Indicator: The Relationship with Economic Growth , 2015 .

[36]  R. Adland,et al.  Multivariate Modeling and Analysis of Regional Ocean Freight Rates , 2017 .

[37]  Xiaolin Song,et al.  Multi-Step Hybrid Prediction Model of Baltic Supermax Index Based on Support Vector Machine , 2016 .

[38]  Fabrice Rossi,et al.  Mean Absolute Percentage Error for regression models , 2016, Neurocomputing.

[39]  Jordi McKenzie,et al.  Mean absolute percentage error and bias in economic forecasting , 2011 .

[40]  Qingcheng Zeng,et al.  A new approach for Baltic Dry Index forecasting based on empirical mode decomposition and neural networks , 2016 .

[41]  Liming Liu,et al.  A Dynamic-Economic Model for Container Freight Market , 2009 .

[42]  Dimitrios D. Thomakos,et al.  The Baltic Dry Index: cyclicalities, forecasting and hedging strategies , 2013 .

[43]  Yong Deng,et al.  A novel method for forecasting time series based on fuzzy logic and visibility graph , 2017, Advances in Data Analysis and Classification.

[44]  Pre-announcements of price increase intentions in liner shipping spot markets , 2017 .

[45]  Lucas Lacasa,et al.  Network structure of multivariate time series , 2014, Scientific Reports.

[46]  Terrence J. Sejnowski,et al.  Unsupervised Learning , 2018, Encyclopedia of GIS.

[47]  Qingsong Ruan,et al.  Cross-correlations between Baltic Dry Index and crude oil prices , 2016 .

[48]  Jørgen Randers,et al.  GREENHOUSE GAS , 2019 .

[49]  M Small,et al.  Complex network from pseudoperiodic time series: topology versus dynamics. , 2006, Physical review letters.

[50]  W. Bessler,et al.  Financing Shipping Companies and Shipping Operations: A Risk‐Management Perspective , 2011 .

[51]  F. Chen,et al.  Long memory and scaling behavior study of bulk freight rate volatility with structural breaks , 2018 .

[52]  Zhongke Gao,et al.  Complex network from time series based on phase space reconstruction. , 2009, Chaos.

[53]  Bekir Şahin,et al.  Forecasting the Baltic Dry Index by using an artificial neural network approach , 2018, TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES.

[54]  C. Lemonakis,et al.  The Effect of Baltic Dry Index, Gold, Oil and USA Trade Balance on Dow Jones Sustainability Index World , 2017 .