mlsanityData to 14 Oct 2026

Comparative Analysis of Deep Learning Models for Real-World ISP Network Traffic Forecasting

arXiv 2503.17410 ↗First posted 20 Mar 2025cs.LG cs.AI cs.NIcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 15 of its 59 references that are in the map. First posted on arXiv 20 Mar 2025.

Builds on

15 of 59 references.

References · 15

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2021Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecastingcited by 1,676
  3. 2022Are Transformers Effective for Time Series Forecasting?cited by 1,424
  4. 2022Transformers in Time Series: A Surveycited by 497
  5. 2020Time Series Forecasting With Deep Learning: A Surveycited by 459
  6. 2019InceptionTime: Finding AlexNet for Time Series Classificationcited by 387
  7. 2020Current Time Series Anomaly Detection Benchmarks are Flawed and are Creating the Illusion of Progresscited by 151
  8. 2022ETSformer: Exponential Smoothing Transformers for Time-series Forecastingcited by 148
  9. 2017LSTM Fully Convolutional Networks for Time Series Classificationcited by 136
  10. 2020Principles and Algorithms for Forecasting Groups of Time Series: Locality and Globalitycited by 70
  11. 2018Deep Gated Recurrent and Convolutional Network Hybrid Model for Univariate Time Series Classificationcited by 26
  12. 2019Insights into LSTM Fully Convolutional Networks for Time Series Classificationcited by 18
3 more
  1. 2024CESNET-TimeSeries24: Time Series Dataset for Network Traffic Anomaly Detection and Forecastingcited by 12
  2. 2020A Survey on Big Data for Network Traffic Monitoring and Analysiscited by 9
  3. 2015A Review of Network Traffic Analysis and Prediction Techniquescited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Which Deep Learner? A Systematic Evaluation of Advanced Deep Forecasting Models Accuracy and Efficiency for Network Traffic Predictioncited by 1
  2. 2026CESNET-TimeSeries26: A Multi-Year Network Traffic Time Series Dataset for Annual Seasonality, Concept Drift, and Long-Term Anomaly Detectioncited by 0