arXiv Machine Learning June 11, 2026 By Satyandra Guthula, Jaber Daneshamooz, Charles Fleming, Kesheng Wu, Walter Willinger, Arpit Gupta

NetBurst: Event-Centric Forecasting of Bursty, Intermittent Time Series

Read the original on arXiv Machine Learning →

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

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CENTILE: A Telemetry Foundation Model Evaluated by the Decisions It Drives

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arXiv Machine Learning

2d ago

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arXiv Statistics ML

2d ago

RACE: Residual-Aware Test-Time Adaptation for Neighbor-Rich Time-Series Foundation Model Forecasting

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arXiv Machine Learning

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arXiv Machine Learning

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arXiv Machine Learning

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CENTILE: A Telemetry Foundation Model Evaluated by the Decisions It Drives

When, Not How Much: Evaluating Time-Series Foundation Models on Sparse Events

RACE: Residual-Aware Test-Time Adaptation for Neighbor-Rich Time-Series Foundation Model Forecasting

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