ArticlePloS one2025
Network traffic prediction based on transformer and temporal convolutional network.
Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Deep learning-based battery health prediction for enhancing electric vehicle performance.Scientific reports · 2026Article
- Closed-Loop digital therapeutics empowered by deep reinforcement learning and wearable sensing for precision orthopedic rehabilitation: a simulation-based proof-of-concept study.Frontiers in rehabilitation sciences · 2026Review
- Integrated spatial-temporal feature alignment with graph convolutional and gated recurrent networks for traffic flow prediction.PloS one · 2026Article
- An effectiveness of deep learning with fox optimizer-based feature selection model for securing cyberattack detection in IoT environments.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This paper proposes a hybrid model combining Transformer and Temporal Convolutional Network (TCN). This model addresses the shortcomings of current approaches in capturing long-term and short-term dependencies in network traffic prediction tasks. The Transformer module effectively captures global temporal relationships through a multi-head self-attention mechanism. Meanwhile, the TCN module models local and long-term dependencies using dilated convolution technology. Experimental results on the PeMSD4 and PeMSD8 datasets demonstrate that our method considerably surpasses current mainstream methods at all time steps, particularly in long-term step prediction. Through ablation experiments, we verified the contribution of each module in the model to the performance, further proving the key role of the Transformer and TCN modules in improving prediction performance.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.