A new approach for Baltic Dry Index forecasting based on empirical mode decomposition and neural networks

Zeng, Qingcheng; Qu, Chenrui; Ng, Adolf K.Y.; Zhao, Xiaofeng

doi:10.1057/mel.2015.2

Cited by 40 publications

(29 citation statements)

References 19 publications

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“…Sahin et al [7] predicted one-step-ahead BDI values by their proposed three artificial neural networks, specifically a univariate model and two bivariate models, by harnessing historical BDI data and the world price of crude oil. Qingcheng et al [8] proposed a decomposition technique for BDI data, and then used a neural network for prediction. Zhang et al [9] compared econometric models such as ARIMA and GARCH with artificial neural network models such as BPNN, RBFNN, and ELM.…”

Section: Related Workmentioning

confidence: 99%

DERN: Deep Ensemble Learning Model for Short- and Long-Term Prediction of Baltic Dry Index

et al. 2020

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The Baltic Dry Index (BDI) is a commonly utilized indicator of global shipping and trade activity. It influences stakeholders’ and ship-owners’ decisions respecting investments, chartering, operational plans, and export and import activities. Accurate prediction of the BDI is very challenging due to its volatility, non-stationarity, and complexity. To help stakeholders and ship-owners make sound short- and long-term maritime business decisions and avoid market risk, we performed short- and long-term predictions of BDI using an ensemble deep-learning approach. In this study, we propose to apply recurrent neural network models for BDI prediction. The state-of-the-art of sequential deep-learning models such as RNN, LSTM, and GRU are employed to predict one- and multi-step-ahead BDI values. In order to increase the accuracy, we assemble the models. In experiments, we compared our results with those of traditional methods such as ARIMA and MLP. The results showed that our proposed method outperforms ARIMA, MLP, RNN, LSTM, and GRU in both short- and long-term prediction of BDI.

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Section: Related Workmentioning

confidence: 99%

DERN: Deep Ensemble Learning Model for Short- and Long-Term Prediction of Baltic Dry Index

et al. 2020

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“…Baltyn [12] aimed to identify the level of diffusion index changes between United States Gross Domestic Product and the BDI for different periods of time which are 90 days. Zeng et al [13] proposed Empirical Mode Decomposition for forecasting of BDI. For this, they decomposed the BDI into three distinct components representing short-term changes, long-run trends, and external shocks respectively.…”

Section: Related Workmentioning

confidence: 99%

Forecasting of Dry Freight Index Data by Using Machine Learning Algorithms

Akyol¹

2019

IJISA

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Discovery of meaningful information from the data and design of an expert system are carried out within the frame of machine learning. Supervised learning is used commonly in practical machine learning. It includes basically two stages: a) the training data are sent to as input to the classifier algorithms, b) the performance of pre-learned algorithm is tested on the testing data. And so, knowledge discovery is carried out through the data. In this study, the analysis of Lloyd data is performed by utilizing Gradient Boosted Trees and Multi-Layer Perceptron learning algorithms. Lloyd data consist of the Baltic Dry Index, Capesize Index, Panamax Index and Supramax Index values, updated daily. Accurate prediction of these data is very important in order to eliminate the risks of commercial organization. Eight datasets from Lloyd data are obtained within the frame of two scenarios: a) the last three index values in the freight index datasets; b) the last three index values in both crude oil price and freight index datasets. The results show that the models designed with Gradient Boosted Trees and Multi-Layer Perceptron algorithms are successful for Lloyd data prediction and so proved their applicability.

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“…ARIMA and the MLP model underperformed. It seems that the above model shows shortcomings in years of radical changes up, like 2005 and 2008-2009 (peak rates); also when forecasting period is longer than one month… Zeng et al [109], forecast BDI, using the "empirical mode decomposition"-EMD (due to Premanode & Toumazou in 2013, and others before) and the "artificial neural networks-ANNs". They found that the "EMD-ANN" outperforms both ANN and VAR.…”

Section: A M Goulielmosmentioning

confidence: 99%

Maritime Economic Modeling: A Survey, 2006-2016

Γουλιέλμος¹

2018

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We surveyed quite a number of papers (~60), where one third of them belonged to maritime economic dynamic systems, meaning multi variable models. The emphasis we placed was on works published the last 11 years or so (2006-2016). In addition, we provided a general picture of maritime economic research from 1973 to 2013. The survey had to be combined with advances in General Econometrics. As a result and despite long delays maritime economists (Glen et al. [1] applied the VAR-vector autoregressive (1987-1988) model due to Sims [2]. But this was not the only one as co-integration [3] [4] dominated the scene given that SEM-simultaneous equations model had defects in relation to exogenous variables. Moreover, VEC-the vector error correction model and a representation of VAR appeared based on Granger's representation theorem. The models for 2006-2016 were 36 including 5 for maritime futures after 2003 and 7 on forecasting (2003-2016). A description of a shipping cycle has been given with the presence of time.

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A new approach for Baltic Dry Index forecasting based on empirical mode decomposition and neural networks

Cited by 40 publications

References 19 publications

DERN: Deep Ensemble Learning Model for Short- and Long-Term Prediction of Baltic Dry Index

DERN: Deep Ensemble Learning Model for Short- and Long-Term Prediction of Baltic Dry Index

Forecasting of Dry Freight Index Data by Using Machine Learning Algorithms

Maritime Economic Modeling: A Survey, 2006-2016

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