Comparative Analysis of Deep Learning Models for Real-World ISP Network Traffic Forecasting
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866913751164256256 |
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| author | Koumar, Josef Smoleň, Timotej Jeřábek, Kamil Čejka, Tomáš |
| author_facet | Koumar, Josef Smoleň, Timotej Jeřábek, Kamil Čejka, Tomáš |
| contents | Accurate network traffic forecasting is essential for Internet Service Providers (ISP) to optimize resources, enhance user experience, and mitigate anomalies. This study evaluates state-of-the-art deep learning models on CESNET-TimeSeries24, a recently published, comprehensive real-world network traffic dataset from the ISP network CESNET3 spanning multivariate time series over 40 weeks. Our findings highlight the balance between prediction accuracy and computational efficiency across different levels of network granularity. Additionally, this work establishes a reproducible methodology that facilitates direct comparison of existing approaches, explores their strengths and weaknesses, and provides a benchmark for future studies using this dataset. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_17410 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Comparative Analysis of Deep Learning Models for Real-World ISP Network Traffic Forecasting Koumar, Josef Smoleň, Timotej Jeřábek, Kamil Čejka, Tomáš Machine Learning Artificial Intelligence Networking and Internet Architecture Accurate network traffic forecasting is essential for Internet Service Providers (ISP) to optimize resources, enhance user experience, and mitigate anomalies. This study evaluates state-of-the-art deep learning models on CESNET-TimeSeries24, a recently published, comprehensive real-world network traffic dataset from the ISP network CESNET3 spanning multivariate time series over 40 weeks. Our findings highlight the balance between prediction accuracy and computational efficiency across different levels of network granularity. Additionally, this work establishes a reproducible methodology that facilitates direct comparison of existing approaches, explores their strengths and weaknesses, and provides a benchmark for future studies using this dataset. |
| title | Comparative Analysis of Deep Learning Models for Real-World ISP Network Traffic Forecasting |
| topic | Machine Learning Artificial Intelligence Networking and Internet Architecture |
| url | https://arxiv.org/abs/2503.17410 |