A survey proposes a novel 3D taxonomy classifying drifts into time stream, data stream, and model stream categories to unify research on non-stationary autonomous learning.
Proactive model adaptation against concept drift for online time series forecasting
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CLOUDADV combines zero-shot forecasting with LLM-generated recommendations for cloud instance sizing, reporting 52.9% simulated monthly cost savings in a seven-VM Azure case study.
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Autonomous Drift Learning in Data Streams: A Unified Perspective
A survey proposes a novel 3D taxonomy classifying drifts into time stream, data stream, and model stream categories to unify research on non-stationary autonomous learning.
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CLOUDADV: Decision-Aligned Instance Sizing with Zero-Shot Foundation Models under Drift
CLOUDADV combines zero-shot forecasting with LLM-generated recommendations for cloud instance sizing, reporting 52.9% simulated monthly cost savings in a seven-VM Azure case study.