arXiv:2510.22397v2 Announce Type: replace-cross Abstract: Network operators monitor their infrastructure by collecting telemetry data such as packet counts, byte rates, or flow volumes, yet answering the questions that effective operations demand -- forecasting future load, diagnosing and characterizing anomalies, and searching for and retrieving historical precedents -- requires more than raw measurements. Bridging this gap calls for learned representations: compact per-entity summaries that capture temporal dynamics from each entity's univariate time series. Time-series foundation models are
The proliferation of complex, distributed internet infrastructure and the increasing reliance on real-time data for operational stability make advanced time series forecasting critical.
This development offers a more efficient and accurate way to manage large-scale network operations, enabling proactive intervention and optimization, which is vital for maintaining robust digital infrastructure.
Traditional monitoring shifts towards predictive, event-centric analytics for bursty network data, allowing for earlier detection of anomalies and more effective resource allocation.
- · Network operators
- · Cloud infrastructure providers
- · AI/ML model developers
- · Telecom companies
- · Legacy network monitoring solutions
- · Organizations with static infrastructure management
Operators gain improved visibility and control over complex and dynamic network environments.
Reduced downtime and enhanced service quality lead to greater operational efficiency and customer satisfaction.
The application of such forecasting models could extend beyond networking to other industries with bursty, intermittent time series data, enabling more resilient and adaptive systems.
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Read at arXiv cs.LG