Aug 2026· 2026 IEEE International Conference on Mechatronics and Automation (ICMA)· pp. 845-849· 0 citations· 15 references
Abstract
The rapid growth of Internet applications has led to increasingly complex network traffic, posing new challenges for accurate long-term forecasting. To address the limitations of traditional linear models in capturing nonlinear temporal dependencies, this paper proposes an improved TSM-Transformer model that introduces a Trend Attention module for long-term tendencies and a Seasonal Attention module for periodic variations. The model also optimizes encoder–decoder fusion to enhance information interaction. Experiments on the Traffic_One_Cell dataset demonstrate that the proposed model achieves the lowest MAE (1.2 × 10⁻3) and RMSE (2.9 × 10⁻3) with an R2 of 92.1%, outperforming ARIMA, GRU, and SVM baselines. The results confirm the model’s superiority in long-horizon network traffic prediction, providing reliable support for proactive resource allocation and network management.
Accurate network traffic forecasting is fundamental to Quality of Service enforcement, proactive congestion control, and dynamic resource allocation in modern backbone and software-defined networks. However, existing approaches often lack adaptability to non-stationary traffic patterns and fail to provide a consistent...
E. Chithra, G. C. Bharathi, S. Allada et al.· International Conference on...· 0 citations
A comparative evaluation of three approaches: Linear Regression, Long Short-Term Memory (LSTM) networks, and Spatio-Temporal Graph Convolutional Network for short-term traffic speed forecasting on the METR-LA benchmark dataset shows that jointly modeling spatial road network topology and temporal traffic dynamics signi...
Dikshya Aryal, Hemant Joshi· Journal of Hillside College...· 0 citations
This paper aims to provide references for subsequent academic exploration and industrial application, helping to enhance the efficiency of urban traffic management, alleviate congestion, ensure safety and promote sustainable development.
The proposed Traffic Demand Spatio-Temporal Graph Transformer (TD-STGT) is a graph neural forecasting framework for predicting changes in wireless mobile traffic demand across fine geographic grids that provides a practical tool for identifying areas with increasing demand pressure and prioritizing future mobile-networ...
The rapid growth in traffic volumes has increased the demand for traffic-flow forecasting models with stronger prediction capability. Traditional methods that rely on local feature extraction and static spatial graph construction can no longer fully meet these requirements. To address the short- and long-term fluctuati...
Hai Yan, Rui Jian, Cheng-Cheng Wang et al.· Algorithms· 0 citations
This study aims to address the issues of overfitting and underutilization of new information in traditional grey models for multi-frequency traffic flow forecasting. It proposes the Recursive Grey Multi-frequency Fourier Model (RGMFM) to enhance the extraction of multi-frequency periodic features and enable dynamic...
Yu Zhang, Lian-Yi Liu, Fei Deng et al.· Grey Systems Theory and Appl...· 0 citations
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