# Which Deep Learner? A Systematic Evaluation of Advanced Deep Forecasting Models Accuracy and Efficiency for Network Traffic Prediction

arXiv: https://arxiv.org/abs/2601.02694 · HTML: https://mlsanity.com/paper/2601.02694/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 55 references that are in the map.
- First posted on arXiv 6 Jan 2026.

## Builds on (most cited first, 19 of 19)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [A Time Series is Worth 64 Words: Long-term Forecasting with Transformers](https://mlsanity.com/paper/2211.14730/) (2022)
- [Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting](https://mlsanity.com/paper/2106.13008/) (2021)
- [An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling](https://mlsanity.com/paper/1803.01271/) (2018)
- [Are Transformers Effective for Time Series Forecasting?](https://mlsanity.com/paper/2205.13504/) (2022)
- [Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting](https://mlsanity.com/paper/1912.09363/) (2019)
- [N-BEATS: Neural basis expansion analysis for interpretable time series forecasting](https://mlsanity.com/paper/1905.10437/) (2019)
- [Long-term Forecasting with TiDE: Time-series Dense Encoder](https://mlsanity.com/paper/2304.08424/) (2023)
- [TimeXer: Empowering Transformers for Time Series Forecasting with Exogenous Variables](https://mlsanity.com/paper/2402.19072/) (2024)
- [Deep Time Series Models: A Comprehensive Survey and Benchmark](https://mlsanity.com/paper/2407.13278/) (2024)
- [Deep Learning for Time Series Forecasting: Tutorial and Literature Survey](https://mlsanity.com/paper/2004.10240/) (2020)
- [An Experimental Review on Deep Learning Architectures for Time Series Forecasting](https://mlsanity.com/paper/2103.12057/) (2021)
- [CESNET-TimeSeries24: Time Series Dataset for Network Traffic Anomaly Detection and Forecasting](https://mlsanity.com/paper/2409.18874/) (2024)
- [A new hope for network model generalization](https://mlsanity.com/paper/2207.05843/) (2022)
- [NeuTM: A Neural Network-based Framework for Traffic Matrix Prediction in SDN](https://mlsanity.com/paper/1710.06799/) (2017)
- [Landing AI on Networks: An equipment vendor viewpoint on Autonomous Driving Networks](https://mlsanity.com/paper/2205.08347/) (2022)
- [Comparative Analysis of Deep Learning Models for Real-World ISP Network Traffic Forecasting](https://mlsanity.com/paper/2503.17410/) (2025)

## Built on by (most cited first, 1 of 1)

- [CESNET-TimeSeries26: A Multi-Year Network Traffic Time Series Dataset for Annual Seasonality, Concept Drift, and Long-Term Anomaly Detection](https://mlsanity.com/paper/2610.14799/) (2026)

## Abstract (arXiv)

Network traffic prediction is essential for automating modern network management. It is a difficult time series forecasting (TSF) problem that has been addressed by Deep Learning (DL) models due to their ability to capture complex patterns. Advances in forecasting, from sophisticated transformer architectures to simple linear models, have improved performance across diverse prediction tasks. However, given the variability of network traffic across network environments and traffic series timescales, it is essential to identify effective deployment choices and modeling directions for network traffic prediction. This study systematically identify and evaluates twelve advanced TSF models -- including transformer-based and traditional DL approaches, each with unique advantages for network traffic prediction -- against three statistical baselines on four real traffic datasets, across multiple time scales and horizons, assessing performance, robustness to anomalies, data gaps, external factors, data efficiency, and resource efficiency in terms of time, memory, and energy. Results highlight performance regimes, efficiency thresholds, and promising architectures that balance accuracy and efficiency, demonstrating robustness to traffic challenges and suggesting new directions beyond traditional RNNs.