Anomaly detection using control flow pattern and fuzzy regression in port container handling

Rahmawati, Dewi; Sarno, Riyanarto

doi:10.1016/j.jksuci.2018.12.004

Cited by 8 publications

(2 citation statements)

References 8 publications

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“…The traditional econometric and statistical methods, including autoregressive integrated moving average (ARIMA) models ( 10 , 11 ), vector error correction model ( 12 ), generalized autoregressive conditional heteroscedasticity model ( 13 ), grey forecast model ( 14 ), exponential smoothing ( 15 ), causal analysis models, such as multiple linear regression ( 16 ), vector autoregression ( 17 ), threshold autoregression ( 18 , 19 ), are widely utilized to predict economic time series. These models have good interpretability and good performance based on linear assumptions.…”

Section: Literature Reviewmentioning

confidence: 99%

Novel Decomposition and Ensemble Model with Attention Mechanism for Container Throughput Forecasting at Four Ports in Asia

Xiao

Xue

et al. 2023

Transportation Research Record: Journal of the Transportation R

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Container shipping has suffered a sharp decline since COVID-19, and risks associated with container transit will persist in the future. The decrease in container transportation has caused a ripple impact on the global supply chain. However, container throughput forecasting is both critical and complicated under the circumstances of economic uncertainty and the outbreak of the COVID-19 pandemic. A novel model propounded in this paper for container throughput forecasting to assist the port management bureau and container shipping industry integrates with the variational mode decomposition (VMD) algorithm, SARIMA technique, convolutional neural network (CNN) method, long short-term memory (LSTM) approach, and attention mechanism, among others. In this model, there are three stages: (i) data decomposition, (ii) component prediction, and (iii) ensemble output. In the first stage, the original data of the container throughput time series is decomposed into several different components using the VMD algorithm. Next, from low frequency to high frequency, each component is modeled by the corresponding prediction approach. Subsequently, the prediction results of each component generated by the previous stage are integrated into the final forecasting results by addition strategy. To enhance the prediction accuracy in the second stage, the attention mechanism is adopted in the CNN-bidirectional LSTM method. Finally, six measurement criteria, the container throughput times series at four ports, and a statistical evaluation approach are applied to comprehensively evaluate the proposed model compared with seven benchmark models. The empirical analysis demonstrates that the proposed model significantly outperforms other comparable models with regard to prediction results, level, and directional prediction accuracy.

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Section: Literature Reviewmentioning

confidence: 99%

Novel Decomposition and Ensemble Model with Attention Mechanism for Container Throughput Forecasting at Four Ports in Asia

Xiao

Xue

et al. 2023

Transportation Research Record: Journal of the Transportation R

View full text Add to dashboard Cite

show abstract

“…Data adalah elemen yang paling dasar, bersifat diskrit, dan belum diproses, sehingga belum memiliki makna. Contoh: angka, kata, kode, tabel, dan basis data [8] [12]. Kemudian terdapat Informasi adalah elemen yang saling terhubung dan merupakan hasil pemrosesan terhadap data, sehingga memiliki suatu makna [9].…”

Section: Gambar 1 Siklus Hidup Sederhana Knowledge Management System (Kms)unclassified

Adoption of Knowledge Management System: A Study on How IGracias Should be Adopted for Telkom Campus

Rahmawati¹,

Istyanto²,

Nasrullah³

2020

komtekinfo

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IGraciass is an integrated academic and non-academic management information system from the Telkom Education Foundation (YPT). Based on the vision and mission, the strategy and tagline of the Telkom Education Foundation is "The Leader of ICT Education Provider" and the spirit of digital transformation for Indonesia, I-Graciass must be adopted and implemented in all higher education institutions (lemdikti) under the auspices. The YPT. However, the facts on the ground the adoption and implementation process of I[1]Graciass in each of these higher education institutions are not easy matters. Many features, ease of use of technology and the system interface needed to access the system are important factors in the success of the system. For this reason, this research was conducted with the aim of facilitating the adoption process of IGraciass in all higher education institutions of the Telkom Education Foundation (YPT) according to the respective needs of the educational institutions under the auspices of YPT. This research was carried out using the mixing method both qualitatively and quantitatively, then the researcher also developed a qualitative research instrument to conduct a survey of the users to determine which channels were effective in the process of transferring knowledge. The results show that to minimize the risk of failure in adopting a knowledge management system, companies need to select a system based on the functional, non-functional and transition requirements for the system, as well as the strength and capacity of the organization.

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The synergistic effect of operational research and big data analytics in greening container terminal operations: A review and future directions

Raeesi

Sahebjamnia

Mansouri

2023

European Journal of Operational Research

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Anomaly detection using control flow pattern and fuzzy regression in port container handling

Cited by 8 publications

References 8 publications

Novel Decomposition and Ensemble Model with Attention Mechanism for Container Throughput Forecasting at Four Ports in Asia

Novel Decomposition and Ensemble Model with Attention Mechanism for Container Throughput Forecasting at Four Ports in Asia

Adoption of Knowledge Management System: A Study on How IGracias Should be Adopted for Telkom Campus

The synergistic effect of operational research and big data analytics in greening container terminal operations: A review and future directions

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