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arXiv Machine Learning By Tianxiang Zhan, Ming Jin, Yuanpeng He, Yuxuan Liang, Shirui Pan

Continuous Evolution Pool: Taming Recurring Concept Drift in Online Time Series Forecasting

Read the original on arXiv Machine Learning →

The paper introduces the Continuous Evolution Pool (CEP), a replay‑free framework for online time series forecasting that tackles recurring concept drift. CEP maintains a dynamic pool of specialized forecasters, using lightweight statistical genes to identify concepts, spawn new models when distribution shifts occur, and prune obsolete ones under memory limits. Experiments on real‑world datasets show CEP reduces forecasting error by up to 24% compared to state‑of‑the‑art baselines, especially in scenarios with pronounced recurring drift.

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.

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