DIC-ST: A Hybrid Prediction Framework Based on Causal Structure Learning for Cellular Traffic and Its Application in Urban Computing

Zhang, Kaisa; Chuai, Gang; Zhang, Jinxi; Chen, Xiangyu; Si, Zhiwei; Maimaiti, Saidiwaerdi

doi:10.3390/rs14061439

Cited by 12 publications

(3 citation statements)

References 39 publications

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“…With the ongoing progress of technology, the utilization of support vector machines (SVMs) and K-nearest neighbors (KNNs) has become widespread in the domain of cellular network traffic prediction [19,20]. These models convert the prediction problem into a linear partition problem, reducing the artificial impact on prediction results and enhancing the prediction accuracy to a certain degree.…”

Section: Related Workmentioning

confidence: 99%

DBSTGNN-Att: Dual Branch Spatio-Temporal Graph Neural Network with an Attention Mechanism for Cellular Network Traffic Prediction

Cai,

Tan,

Zhang

et al. 2024

Applied Sciences

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As network technology continues to develop, the popularity of various intelligent terminals has accelerated, leading to a rapid growth in the scale of wireless network traffic. This growth has resulted in significant pressure on resource consumption and network security maintenance. The objective of this paper is to enhance the prediction accuracy of cellular network traffic in order to provide reliable support for the subsequent base station sleep control or the identification of malicious traffic. To achieve this target, a cellular network traffic prediction method based on multi-modal data feature fusion is proposed. Firstly, an attributed K-nearest node (KNN) graph is constructed based on the similarity of data features, and the fused high-dimensional features are incorporated into the graph to provide more information for the model. Subsequently, a dual branch spatio-temporal graph neural network with an attention mechanism (DBSTGNN-Att) is designed for cellular network traffic prediction. Extensive experiments conducted on real-world datasets demonstrate that the proposed method outperforms baseline models, such as temporal graph convolutional networks (T-GCNs) and spatial–temporal self-attention graph convolutional networks (STA-GCNs) with lower mean absolute error (MAE) values of 6.94% and 2.11%, respectively. Additionally, the ablation experimental results show that the MAE of multi-modal feature fusion using the attributed KNN graph is 8.54% lower compared to that of the traditional undirected graphs.

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Section: Related Workmentioning

confidence: 99%

DBSTGNN-Att: Dual Branch Spatio-Temporal Graph Neural Network with an Attention Mechanism for Cellular Network Traffic Prediction

Cai,

Tan,

Zhang

et al. 2024

Applied Sciences

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show abstract

“…In addition, different types of time series data have different requirements for the length of the time series used to calculate the TE. For example, a study by Zhang et al [49] found that for different components of the same time series, the causal relationships between them differed in nature.…”

Section: Determination Of the Parameters For Transfer Entropymentioning

confidence: 99%

“…From the figure, it is evident that the time series data of largescale industrial systems, as represented by the satellite, exhibits significant periodicity. Therefore we choose an integer multiple of time series data period as the total length of the time series data for estimating the TE, drawing on the study by Zhang et al [49].…”

Section: Determination Of the Parameters For Transfer Entropymentioning

confidence: 99%

Anomaly detection model for large-scale industrial systems using transfer entropy and graph attention network

Liang,

Pi,

Zhang

2024

Meas. Sci. Technol.

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Multivariate time series (MTS) anomaly detection is vital for ensuring the safety and reliability of large-scale industrial systems. However, existing deep learning methods often overlook complex interrelationships between different time series and the study of anomalies has been limited to detection. To address this, we propose an MTS anomaly detection model based on transfer entropy (TE) and graph attention network (GAT). In the graph construction module, by combining modified TE with automatic structure learning, we extract intricate relationships between features. In the prediction module, we modify the GAT to implement the dynamic attention mechanism and non-linear interaction between different features to improve the accuracy of model prediction. Finally, our model combines the modified TE with anomaly detection task, which can be used to provide interpretability for the detected anomalies using the constructed causal graph. Experimental results on both real and public datasets show that our approach outperforms the mainstream methods, in particular, achieving optimal results in terms of F1 scores and recall.

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Traffic Congestion Prediction: A Machine Learning Approach

Geromichalou,

Mystakidis,

Tjortjis

2024

Lecture Notes in Networks and Systems

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DIC-ST: A Hybrid Prediction Framework Based on Causal Structure Learning for Cellular Traffic and Its Application in Urban Computing

Cited by 12 publications

References 39 publications

DBSTGNN-Att: Dual Branch Spatio-Temporal Graph Neural Network with an Attention Mechanism for Cellular Network Traffic Prediction

DBSTGNN-Att: Dual Branch Spatio-Temporal Graph Neural Network with an Attention Mechanism for Cellular Network Traffic Prediction

Anomaly detection model for large-scale industrial systems using transfer entropy and graph attention network

Traffic Congestion Prediction: A Machine Learning Approach

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