Forecasting period charter rates of VLCC tankers through neural networks: A comparison of alternative approaches

Santos, André Alves Portela; Junkes, Luciano N; Pires, Floriano C. M.

doi:10.1057/mel.2013.20

Cited by 26 publications

(19 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Nesse sentido, espera-se que seja positiva a relação entre a produção de petróleo mundial e o frete. Santos et al (2014) também utilizaram a produção de petróleo mundial como variável explicativa do valor do aluguel do VLCC para TCP 1 ano e TCP 3 anos. Portanto, o primeiro passo para calcular o MAPE out-of-sample do modelo foi prever as variáveis causais selecionadas para o modelo, a saber: (i) produção de petróleo mundial, (ii) importação de petróleo EUA e (iii) frota VLCC.…”

Section: Serie_td15_wsunclassified

“…O modelo de regressão dinâmica possui a seguinte equação: (Geomelos e Xideas, 2014;Li e Parsons, 1997;Santos et al, 2014;Tsioumas et al, 2017). O cenário pessimista consiste em uma redução da produção mundial de petróleo de 1% no período de seis meses, redução da importação de petróleo pelos EUA e a manutenção da frota de navios do tipo VLCC.…”

Section: Figura -Previsão Da Variável Causal Frota Vlcc (Mmdwt)unclassified

“…, concluíram que o frete e o laycan estão interrelacionados e devem ser modelados como um sistema de equações simultâneas.Outras conclusões do estudo: (i) Na média, as taxas frete de navios de casco simples apresentam um desconto em relação aos navios de casco duplo; (ii) as taxas fretes são positivamente e negativamente relacionadas ao comprimento do período de laycan e ao fator de utilização, respectivamente; (iii) taxa frete de navio-tanque e período de laycan variam de acordo com a rota; (iv) os períodos de laycan dos contratos de navios-tanque variam diretamente com as taxas frete e indiretamente com a volatilidade da taxa frete; e (v) períodos de laycan de naviostanque estão negativamente relacionados com as idades dos navios. Para comparar os resultados das redes neurais com uma técnica tradicional de previsão, modelos de séries temporais (média móvel auto-regressivo ou ARMA) foram derivados e os resultados das redes neurais se mostraram consistentemente superiores ao modelo ARMA, especialmente para previsão de médio e longo prazo Santos et al (2014). verificaram o desempenho das redes neurais artificiais (RNA) como ferramenta de previsão para séries temporais de valor de aluguel em contratos de 1 e 3 anos de navios petroleiros do tipo VLCC.…”

unclassified

See 2 more Smart Citations

Forecasting Tanker Freight Rate

Bertoloto

Oliveira

2019

Operations Management for Social Good

View full text Add to dashboard Cite

Crude oil and oil products seaborne transportation is a key component of the petroleum industry supply chain, integrating suppliers and customers located in different geographic regions. In this context, the freight rates practiced have a great impact on the international trade of these goods. This work aims to verify the performance of Dynamic Regression models in short-term maritime freight forecasts of the spot market of an oil export route from West Africa to China, to compare the predictive capacity of the model with traditional methods, widely discussed in the literature, such as Exponential Smoothing and ARIMA models and to design scenarios to evaluate how the explanatory variables present in the Dynamic Regression model proposed in this study affect freight rate. The product developed in this dissertation showed the viability of the univariate and causal models being used as a forecasting tool for the oil tankers freight rate. As a form of validation, the results were compared to those obtained with the methodology of a large Brazilian oil company. The proposed prediction system prototype, through Dynamic Regression model, presented satisfactory results and performance superior to that obtained through the methodology of the oil company.

show abstract

Section: Serie_td15_wsunclassified

Section: Figura -Previsão Da Variável Causal Frota Vlcc (Mmdwt)unclassified

unclassified

See 1 more Smart Citation

Forecasting Tanker Freight Rate

Bertoloto

Oliveira

2019

Operations Management for Social Good

View full text Add to dashboard Cite

show abstract

“…In recent years, the use of artificial neural networks (ANNs) has grown rapidly due to their ability to map any linear or non-linear function. ANNs also do not have any associated data assumption requirements (Kunt et al, 2011;Santos et al, 2014). ANNs are also specifically designed to handle a very large, complex systems that have many inter-related parameters (Kalogirou, 2014).…”

Section: Introductionmentioning

confidence: 99%

Modelling Australia’s Outbound Passenger Air Travel Demand Using an Artificial Neural Network Approach

2017

IJTTE

View full text Add to dashboard Cite

This paper focuses on predicting Australia's outbound international airline passenger demand using an artificial neural network (ANN) modelling method. The modelling in the study was based on annual data for the period 1993 to 2016. The model was developed using the input parameters of world GDP, world population growth, world jet fuel prices, world air fares (proxy for air travel cost), Australia's tourism attractiveness, outbound flights, Australia's unemployment levels, the Australian and United States foreign exchange rate and three dummy variables (Sydney Olympics, 9/11 and the 2006 Commonwealth Games). The artificial neural network (ANN) used multi-layer perceptron (MLP) architecture that compromised a multi-layer feed-forward network and the sigmoid and linear functions were used as activation functions with the feed forward-back propagation algorithm. The ANN was applied during training, testing and validation and had 8 inputs, 1 neurons in the hidden layers and 1 neuron in the output layer. The data was randomly divided into three data sets; training, testing and model validation. The best-fit model was selected according to four goodness-of-fit measures: mean absolute error (MAE), mean square error (MSE), root mean square errors (RMSE), AND mean absolute percentage errors (MAPE). The highest R-value obtained from the ANN model is 0.99733, demonstrating that the ANN provided a high predictive capability.

show abstract

“…In recent years, the use of artificial neural networks has grown rapidly due to its ability of mapping any linear or non-linear function and having no associated data assumption requirements (Claveria, Torra 2014;Kunt et al 2011;Santos et al 2014). This technique consists of training a computer to learn from substantial data based on the structure of human brain, using many simple processing elements (Haykin 1999).…”

Section: Introductionmentioning

confidence: 99%

Forecasting Demand for Low Cost Carriers in Australia Using an Artificial Neural Network Approach

2015

View full text Add to dashboard Cite

This study focuses on predicting Australia's low cost carrier passenger demand and revenue passenger kilometres performed (RPKs) using traditional econometric and artificial neural network (ANN) modelling methods. For model development, Australia's real GDP, real GDP per capita, air fares, Australia's population and unemployment, tourism (bed spaces) and 4 dummy variables, utilizing quarterly data obtained between 2002 and 2012, were selected as model parameters. The neural network used multi-layer perceptron (MLP) architecture that compromised a multi-layer feed-forward network and the sigmoid and linear functions were used as activation functions with the feed forward-back propagation algorithm. The ANN was applied during training, testing and validation and had 11 inputs, 9 neurons in the hidden layers and 1 neuron in the output layer. When comparing the predictive accuracy of the two techniques, the ANNs provided the best prediction and showed that the performance of the ANN model was better than that of the traditional multiple linear regression (MLR) approach. The highest R-value for the enplaned passengers ANN was around 0.996 and for the RPKs ANN was round 0.998, respectively.

show abstract

Forecasting period charter rates of VLCC tankers through neural networks: A comparison of alternative approaches

Cited by 26 publications

References 18 publications

Forecasting Tanker Freight Rate

Forecasting Tanker Freight Rate

Modelling Australia’s Outbound Passenger Air Travel Demand Using an Artificial Neural Network Approach

Forecasting Demand for Low Cost Carriers in Australia Using an Artificial Neural Network Approach

Contact Info

Product

Resources

About