A model for port throughput forecasting using Bayesian estimation

Eskafi, Majid; Kowsari, Milad; Dastgheib, Ali; Úlfarsson, Gudmundur F.; Stefánsson, Gunnar; Taneja, P.; Thorarinsdottir, Ragnheiður I.

doi:10.1057/s41278-021-00190-x

Cited by 13 publications

(6 citation statements)

References 56 publications

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“…Chen and Chen [16] studied port container throughput prediction using the genetic planning-based approach. Eskafi et al [17] predicted the port throughput using Bayesian estimation models taking epistemic uncertainty into account affecting macroeconomic variables to forecast the annual throughput of the multipurpose Port of Isafjordur in Iceland. (4) Combined Forecasting Method.…”

Section: Influencing Factors and Various Methodologies On Thementioning

confidence: 99%

Container Throughput Forecasting of Tianjin-Hebei Port Group Based on Grey Combination Model

Wang

2021

Journal of Mathematics

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Container throughput forecasting plays an important role in port capacity planning and management. Regarding the issue of container throughput of Tianjin-Hebei Port Group, considering the container throughput is an incomplete grey information system affected by various factors, the effect is often unsatisfactory by adopting a single forecasting model. Therefore, this paper studies the issue by combining fractional GM (1, 1) and BP neural network. The comparison results show that the combination model performs better than other single models separately and has a higher level of forecasting accuracy. Furthermore, the combination model is adopted to forecast the container throughput of Tianjin-Hebei Port Group from 2021 to 2025, which would be a data reference for the future development optimization for the container operation of Tianjin-Hebei Port Group.

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Section: Influencing Factors and Various Methodologies On Thementioning

confidence: 99%

Container Throughput Forecasting of Tianjin-Hebei Port Group Based on Grey Combination Model

Wang

2021

Journal of Mathematics

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“…The related uncertainty is quantified by the posterior distribution. However, this prediction method requires effective quantification of macroeconomic variables [31]. Kowsari et al pointed out that the Bayesian method regards the regression coefficient as a random variable and considers the uncertainty of the parameters with the data as the condition.…”

Section: Review Of Demand Forecastingmentioning

confidence: 99%

Bayesian Network Demand-Forecasting Model Based on Modified Particle Swarm Optimization

Hu,

2023

Applied Sciences

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With the increasing variety of products, the increasing substitutability of products, and the trend of customized products, the volatility of market demand is increasing, which poses a challenge to make accurate demand forecasting. The Bayesian method is particularly promising and appealing when the data fluctuate greatly. This paper proposes a product-demand forecasting model based on multilayer Bayesian network, which introduces hidden layer variables and volatility factors to meet the time series connection and volatility of the demand data. However, most studies use sampling methods to estimate the parameters. We use Bayesian maximum a posteriori estimation to estimate the model parameters and introduce an improved particle swarm optimization algorithm (MPSO) to optimize the objective function. In order to increase the diversity of the particle population and accelerate the convergence, an adaptive particle velocity, position updating strategy, and nonlinear changing inertia weight are introduced in the algorithm. Finally, RMSE (Root Mean Square Error) and MAPE (Mean Absolute Percentage Error) are used as the evaluation criterion to conduct experiments on six different datasets, and the experimental results are compared with the results of the ARIMA (autoregressive integrated moving average model) method and PSO algorithm. The experimental results show that the method has a good prediction effect. It provides a new idea for demand forecasting in the supply chain.

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“…With the help of more features, the model achieves better results than the time series mod-el. M. Eskafi et al [15] applied a Bayesian statistical method to forecast the annual throughput of the multipurpose port of Isafjordur in Iceland. In this model, the national GDP (NGDP), the average yearly CPI (ACPI), the world GDP (WGDP), the volume of national export trade (VNET), the volume of national import trade (VNIT), and the national population (NPOP) are concerned.…”

Section: Related Workmentioning

confidence: 99%

Long Short-Term Memory and Graph Convolution Network for Forecasting the Crude Oil Traffic Flow

Ouyang¹,

Sun²,

Xue³

et al. 2022

IEEE Access

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Understanding maritime network structure and traffic flow changes is a challenging task that must incorporate economic, energy, geopolitics, maritime transportation, and network sciences. Crude oil is the most imported energy in the world. Investigating the crude oil maritime network status and predicting the crude oil traffic flow changes has great significance for the global trade, especially for key crude oil importing/exporting regions and countries. To address this, a system-based approach using long short-term memory and graph convolution network for the crude oil traffic flow forecasting named LGCOTFF is introduced. The LGCOTFF approach constructs a maritime transportation network firstly, and then calculates and predicts the node traffic flow based on trajectory data and crude oil berth geographical position. Firstly, we construct a maritime crude oil transportation network based on supply-demand relationship, ship trajectory and route information. Then, we design an approach to calculate how many crude oil ships finished up-load/offtake tasks in a single week for each port, and gather this data to countries and regions. Finally, we design a deep learning neural network named long short-term memory and graph convolution network (L-GCN) to extract the temporal and spatial characteristics of crude oil transportation, and predict the node traffic flow. We evaluate the proposed model on China, Russia, Middle East and America respectively and observe consistent improvement of more than 10% over state-of-the-art baselines.

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A model for port throughput forecasting using Bayesian estimation

Cited by 13 publications

References 56 publications

Container Throughput Forecasting of Tianjin-Hebei Port Group Based on Grey Combination Model

Container Throughput Forecasting of Tianjin-Hebei Port Group Based on Grey Combination Model

Bayesian Network Demand-Forecasting Model Based on Modified Particle Swarm Optimization

Long Short-Term Memory and Graph Convolution Network for Forecasting the Crude Oil Traffic Flow

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