# CESNET-TimeSeries26: A Multi-Year Network Traffic Time Series Dataset for Annual Seasonality, Concept Drift, and Long-Term Anomaly Detection

arXiv: https://arxiv.org/abs/2610.14799 · HTML: https://mlsanity.com/paper/2610.14799/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 47 references that are in the map.
- First posted on arXiv 10 Oct 2026.

## Builds on (most cited first, 15 of 15)

- [Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting](https://mlsanity.com/paper/2012.07436/) (2020)
- [Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting](https://mlsanity.com/paper/2106.13008/) (2021)
- [Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks](https://mlsanity.com/paper/1703.07015/) (2017)
- [Current Time Series Anomaly Detection Benchmarks are Flawed and are Creating the Illusion of Progress](https://mlsanity.com/paper/2009.13807/) (2020)
- [LargeST: A Benchmark Dataset for Large-Scale Traffic Forecasting](https://mlsanity.com/paper/2306.08259/) (2023)
- [Time-IMM: A Dataset and Benchmark for Irregular Multimodal Multivariate Time Series](https://mlsanity.com/paper/2506.10412/) (2025)
- [CESNET-TimeSeries24: Time Series Dataset for Network Traffic Anomaly Detection and Forecasting](https://mlsanity.com/paper/2409.18874/) (2024)
- [A Survey on Big Data for Network Traffic Monitoring and Analysis](https://mlsanity.com/paper/2003.01648/) (2020)
- [NetTiSA: Extended IP Flow with Time-series Features for Universal Bandwidth-constrained High-speed Network Traffic Classification](https://mlsanity.com/paper/2310.05530/) (2023)
- [When Simple Model Just Works: Is Network Traffic Classification in Crisis?](https://mlsanity.com/paper/2506.08655/) (2025)
- [Comparative Analysis of Deep Learning Models for Real-World ISP Network Traffic Forecasting](https://mlsanity.com/paper/2503.17410/) (2025)
- [Time-Series Foundation Models for ISP Traffic Forecasting](https://mlsanity.com/paper/2511.17529/) (2025)
- [CENTILE: A Telemetry Foundation Model Evaluated by the Decisions It Drives](https://mlsanity.com/paper/2608.01725/) (2026)
- [Sustainability or Survivability? Eliminating the Need to Choose in LEO Satellite Constellations](https://mlsanity.com/paper/2508.17763/) (2025)
- [Which Deep Learner? A Systematic Evaluation of Advanced Deep Forecasting Models Accuracy and Efficiency for Network Traffic Prediction](https://mlsanity.com/paper/2601.02694/) (2026)

## Abstract (arXiv)

Forecast-based models are fundamental to network traffic management and anomaly detection. However, it is essential to evaluate these forecasting models over extended periods of time in order to assess their adaptability to unexpected phenomena and shifts in underlying patterns. This kind of long-term evaluation requires multi-year data to account for yearly seasonality and behavioral drift. Existing public datasets are too short to support such long-horizon forecasting tasks. We introduce CESNET-TimeSeries26, comprising 995 days of continuous traffic time series from the CESNET3 network, together with a single subnet spanning approximately 4.5 years. The dataset captures 278,466 IP addresses, 285 institutions, and 566 institutional subnets at three aggregation intervals. The dataset provides a definitive longitudinal benchmark for evaluating forecasting models in both operational management and security monitoring. We demonstrate that annual seasonality is the dominant periodic component. Moreover, we show that a forecasting model can exploit the annual component only when its training window covers at least one complete annual cycle, which no sub-year dataset provides.