An Optimal Model based on Multifactors for Container Throughput Forecasting

Tang, Shuang; Xu, Sudong; Gao, Jianwen

doi:10.1007/s12205-019-2446-3

Cited by 24 publications

(9 citation statements)

References 29 publications

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“…According to the continuous resampling of the model particles of the import and export trade data, the iterative prediction and weight update continuously select the model particle with the largest number as the optimal multicore RVM. 25,26 Then apply it to our PSO to optimize the import and export of the hybrid RVM model.…”

Section: Rvm Modelmentioning

confidence: 99%

Import and export trade forecasting algorithm based on blockchain security andPSOoptimized hybrid RVM model

Wang

2022

Security and Privacy

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With the rapid development of globalization economy, countries in the world are more and more closely connected in business, and international import and export trade has also achieved rapid development. Based on the analysis of the status quo of the country's import and export trade, this article combines the main national economic indicators to analyze and study the algorithm of import and export trade forecasting. RVM is a sparse probability model and a new supervised learning method. Firstly, this paper introduces the protection system of block chain technology for import and export trade data information. Then, a hybrid RVM model is constructed, which is optimized by PSO algorithm, and the import and export volume is predicted based on this model. Based on the analysis of the data, it is tested that the security protection coefficient of the blockchain security technology for import and export trade data can reach up to 99.9%, and the accuracy of the import and export trade forecast based on the PSO optimized hybrid RVM model can reach up to 85.79%. A series of experiments show that the import and export trade forecast model studied in this paper can accurately predict the volume of import and export trade, thus providing a new method for the country's import and export trade, with high practical significance.

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Section: Rvm Modelmentioning

confidence: 99%

Import and export trade forecasting algorithm based on blockchain security andPSOoptimized hybrid RVM model

Wang

2022

Security and Privacy

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“…Tsai and Huang (2017) discussed the usage of artificial neural network (ANN) to predict container flows among major Asian ports while considering the impact of GDP, interest rates, the value of export and import trade, the numbers of export and import containers, and the number of quay cranes. Regarding the external influential factors, Tang et al (2019) established BP neural network to forecast the container throughput of Shanghai port and Lianyungang port. However, the performance of the traditional artificial intelligence models has to be enhanced further because of the intrinsic drawbacks such as weak global search ability, sluggish convergence, and easy fall into a local minimum.…”

Section: Introductionmentioning

confidence: 99%

Effective multi‐step ahead container throughput forecasting under the complex context

Xiao

Xie

et al. 2023

Journal of Forecasting

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Accurate and effective container throughput forecasting plays an essential role in economic dispatch and port operations, especially in the complex and uncertain context of the global Covid‐19 pandemic. In light of this, this research proposes an effective multi‐step ahead forecasting model called EWT‐TCN‐KMSE. Specifically, we initially use the empirical wavelet transform (EWT) to decompose the original container throughput series into multiple components with varying frequencies. Subsequently, the state‐of‐the‐art temporal convolutional network is utilized to predict the decomposed components individually, during which an improved loss function that combines mean square error (MSE) and kernel trick is employed. Eventually, the deduced prediction results can be obtained by integrating the predicted values of each component. In particular, this research introduces the MIMO (multi‐input and multi‐output) strategy to conduct multi‐step ahead container throughput forecasting. Based on the experiments in Shanghai port and Ningbo‐Zhoushan port, it can be found that the proposed model shows its superiority over benchmark models in terms of accuracy, stability, and significance in container throughput forecasting. Therefore, our proposed model can assist port operators in their daily management and decision making.

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“…It can be seen from the above literature summary that Chinese scholars use diversified model methods to predict the demand for information service talents. ere are the exponential change method, the nonlinear regression model, the binary linear regression model, GM (1, 1) model, and the exponential smoothing method [17].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Information Talent Demand Based on the Grayscale Prediction Model and the BP Neural Network

Sun

2022

Mobile Information Systems

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With the increasing popularity of the Internet technology, people are now increasingly accustomed to obtaining information or help through the Internet. Meanwhile, the great development of the information service industry has led to the explosive growth of the demand for information service talents. In recent years, many information service talent demand reports have been released in China, and it has an important guiding significance for information service industry planning. However, there are three problems with the information service industry talent demand reports at present. First, the relevance and support of talent demand analysis and forecast to information service industry planning need to be clarified. Second, the coordination and cooperation of information service personnel demand report preparation need to be improved. The third is the wider application of scientific and reasonable information service personnel demand forecasting models. In the future, we need to develop and use more reasonable information service personnel demand forecasting models and improve the quality of information service personnel demand reports. At the same time, the supporting role of the information service industry in scientific planning needs to be strengthened continuously. Therefore, information service industry talent demand forecasting is of great significance. In this paper, a prediction model of information service talent demand is established by using gray system theory. For the deviations of the GM (1, 1) model, a combined GM (1, 1)-BP neural network prediction model is proposed. The simulation results show that the prediction results of the prediction model in this paper are satisfactory. Therefore, the GM (1, 1)-BP model proposed in this paper can be used as a reference for government decision-making and information service personnel training.

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An Optimal Model based on Multifactors for Container Throughput Forecasting

Cited by 24 publications

References 29 publications

Import and export trade forecasting algorithm based on blockchain security andPSOoptimized hybrid RVM model

Import and export trade forecasting algorithm based on blockchain security andPSOoptimized hybrid RVM model

Effective multi‐step ahead container throughput forecasting under the complex context

Prediction of Information Talent Demand Based on the Grayscale Prediction Model and the BP Neural Network

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