[1]

Song X, Chen ZS. 2024. Shipping market time series forecasting via an Ensemble Deep Dual-Projection Echo State Network. Computers and Electrical Engineering 117:109218

doi: 10.1016/j.compeleceng.2024.109218
[2]

Song X, Chen ZS. 2024. Enhancing financial time series forecasting in the shipping market: a hybrid approach with Light Gradient Boosting Machine. Engineering Applications of Artificial Intelligence 136:108942

doi: 10.1016/j.engappai.2024.108942
[3]

Rothbarth E. 1939. Tanker freight rates and tankship building. The Economic Journal 49(196):760−62

doi: 10.2307/2225041
[4]

Zannetos ZS. 1959. The theory of oil tankship rates. Doctoral dissertation. Massachusetts Institute of Technology, U.S. pp. 1−299
[5]

Eslami P, Jung K, Lee D, Tjolleng A. 2017. Predicting tanker freight rates using parsimonious variables and a hybrid artificial neural network with an adaptive genetic algorithm. Maritime Economics & Logistics 19(3):538−50

doi: 10.1057/mel.2016.1
[6]

Goulielmos AM, Psifia ME. 2009. Forecasting weekly freight rates for one-year time charter 65 000 dwt bulk carrier, 1989–2008, using nonlinear methods. Maritime Policy & Management 36(5):411−36

doi: 10.1080/03088830903187150
[7]

Li J, Parsons MG. 1997. Forecasting tanker freight rate using neural networks. Maritime Policy & Management 24(1):9−30

doi: 10.1080/03088839700000053
[8]

Lyridis DV, Zacharioudakis P, Mitrou P, Mylonas A. 2004. Forecasting tanker market using artificial neural networks. Maritime Economics & Logistics 6(2):93−108

doi: 10.1057/palgrave.mel.9100097
[9]

Santos AAP, Junkes LN, Pires FCM Jr. 2014. Forecasting period charter rates of VLCC tankers through neural networks: A comparison of alternative approaches. Maritime Economics & Logistics 16(1):72−91

doi: 10.1057/mel.2013.20
[10]

Yang Z, Mehmed EE. 2019. Artificial neural networks in freight rate forecasting. Maritime Economics & Logistics 21(3):390−414

doi: 10.1057/s41278-019-00121-x
[11]

Zhang J, Zeng Q, Zhao X. 2014. Forecasting spot freight rates based on forward freight agreement and time charter contract. Applied Economics 46(29):3639−48

doi: 10.1080/00036846.2014.937038
[12]

Hale C, Vanags A. 1989. Spot and period rates in the dry bulk market: Some tests for the period 1980−1986. Journal of Transport Economics and Policy 23(3):281−91
[13]

Glen D, Owen M, Van der Meer R. 1981. Spot and time charter rates for tankers, 1970−77. Journal of Transport Economics and Policy 15(1):45−58
[14]

Strandenes SP. 1984. Price determination in the time charter and second hand markets. Working Paper MU 6:15. Norway: Center for Applied Research, Norwegian School of Economics and Business Administration
[15]

Beenstock M, Vergottis A. 1989. An econometric model of the world tanker market. Journal of Transport economics and Policy 23:263−80
[16]

Beenstock M, Vergottis A. 1993. Econometric modelling of world shipping. First Edition, ed. Homa MS. Dordrecht, Netherlands: Springer Science & Business Media
[17]

Glen D, Martin B. 2005. A survey of the modelling of dry bulk and tanker markets. Research in Transportation Economics 12(1):19−64
[18]

Veenstra AW, 1999. Quantitative analysis of shipping markets. Thesis. Erasmus Universiteit Rotterdam, Netherlands. pp. 1−266
[19]

Kavussanos MG. 1996. Comparisons of volatility in the dry-cargo ship sector: Spot versus time charters, and smaller versus larger vessels. Journal of Transport economics and Policy 30(1):67−82
[20]

Bulut E, Duru O, Yoshida S. 2012. 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. Maritime Economics & Logistics 14(3):300−18

doi: 10.1057/mel.2012.9
[21]

Chen ZS, Lam JSL, Xiao Z. 2023. Prediction of harbour vessel fuel consumption based on machine learning approach. Ocean Engineering 278:114483

doi: 10.1016/j.oceaneng.2023.114483
[22]

Chen ZS, Lam JSL, Xiao Z. 2024. Prediction of harbour vessel emissions based on machine learning approach. Transportation Research Part D: Transport and Environment 131:104214

doi: 10.1016/j.trd.2024.104214
[23]

Duru O, Bulut E. 2014. A non-linear clustering method for fuzzy time series: Histogram damping partition under the optimized cluster paradox. Applied Soft Computing 24:742−48

doi: 10.1016/j.asoc.2014.08.038
[24]

Gao R, Du L, Duru O, Yuen KF. 2021. Time series forecasting based on echo state network and empirical wavelet transformation. Applied Soft Computing 102:107111

doi: 10.1016/j.asoc.2021.107111
[25]

Gao R, Du L, Yuen KF. 2020. Robust empirical wavelet fuzzy cognitive map for time series forecasting. Engineering Applications of Artificial Intelligence 96:103978

doi: 10.1016/j.engappai.2020.103978
[26]

Qiu X, Ren Y, Suganthan PN, Amaratunga GAJ. 2017. Empirical mode decomposition based ensemble deep learning for load demand time series forecasting. Applied Soft Computing 54:246−55

doi: 10.1016/j.asoc.2017.01.015
[27]

Syriopoulos T, Tsatsaronis M, Karamanos I. 2021. Support vector machine algorithms: an application to ship price forecasting. Computational Economics 57(1):55−87

doi: 10.1007/s10614-020-10032-2
[28]

Zeng Q, Qu C, Ng AKY, Zhao X. 2016. A new approach for Baltic Dry Index forecasting based on empirical mode decomposition and neural networks. Maritime Economics & Logistics 18(2):192−210

doi: 10.1057/mel.2015.2
[29]

Duru O. 2010. A fuzzy integrated logical forecasting model for dry bulk shipping index forecasting: an improved fuzzy time series approach. Expert Systems with Applications 37(7):5372−80

doi: 10.1016/j.eswa.2010.01.019
[30]

Bao J, Pan L, Xie Y, 2016. A new BDI forecasting model based on support vector machine. 2016 IEEE Information Technology, Networking, Electronic and Automation Control Conference, Chongqing, China, 20−22 May 2016. USA: IEEE. pp. 65−69 doi: 10.1109/ITNEC.2016.7560320
[31]

Han Q, Yan B, Ning G, Yu B. 2014. Forecasting dry bulk freight index with improved SVM. Mathematical Problems in Engineering 2014:460684

doi: 10.1155/2014/460684
[32]

Chou CC, Lin KS. 2019. A fuzzy neural network combined with technical indicators and its application to Baltic Dry Index forecasting. Journal of Marine Engineering & Technology 18(2):82−91

doi: 10.1080/20464177.2018.1495886
[33]

Kamal IM, Bae H, Sunghyun S, Yun H. 2020. DERN: Deep ensemble learning model for short-and long-term prediction of Baltic dry index. Applied Sciences 10(4):1504

doi: 10.3390/app10041504
[34]

Şahin B, Gürgen S, Ünver B, Altin I. 2018. Forecasting the Baltic Dry Index by using an artificial neural network approach. Turkish Journal of Electrical Engineering & Computer Sciences 26(3):1673−84

doi: 10.3906/elk-1706-155
[35]

Wong HL. 2014. BDI forecasting based on fuzzy set theory, grey system and ARIMA. Proc. XXVII International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, Kaohsiung, Taiwan, China, 2014. 27. Springer International Publishing. pp. 140−49 doi: 10.1007/978-3-319-07455-9
[36]

Zhang GP. 2003. Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 50:159−175

doi: 10.1016/S0925-2312(01)00702-0
[37]

Liu C, Hu Z, Li Y, Liu S. 2017. Forecasting copper prices by decision tree learning. Resources Policy 52:427−34

doi: 10.1016/j.resourpol.2017.05.007
[38]

Bekiroglu K, Duru O, Gulay E, Su R, Lagoa C. 2018. Predictive analytics of crude oil prices by utilizing the intelligent model search engine. Applied Energy 228:2387−97

doi: 10.1016/j.apenergy.2018.07.071
[39]

Bergmeir C, Benítez JM. 2012. On the use of cross-validation for time series predictor evaluation. Information Sciences 191:192−213

doi: 10.1016/j.ins.2011.12.028
[40]

Hyndman RJ, Koehler AB. 2006. Another look at measures of forecast accuracy. International Journal of Forecasting 22(4):679−88

doi: 10.1016/j.ijforecast.2006.03.001