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

Abstract

Recurring concept drift is pervasive in real-world online time series, where the underlying data-generating process repeatedly alternates between a small set of regimes, most notably daily or seasonal cycles that dominate energy, traffic, and weather patterns, and is therefore a central obstacle to reliable long-horizon forecasting. This problem poses a dual challenge in online time series forecasting: mitigating catastrophic forgetting while operating under strict constraints that prevent storing or replaying historical raw samples. Existing approaches predominantly rely on parameter updates or experience replay, which inevitably suffer from knowledge overwriting or stale replay buffers. To address this, the Continuous Evolution Pool (CEP), a replay-free framework that maintains a dynamic pool of specialized forecasters, is proposed. Instead of storing raw samples, CEP utilizes lightweight statistical genes to decouple concept identification from forecasting. Specifically, it employs a retrieval mechanism to identify the nearest concept based on gene similarity, an evolution strategy to spawn new forecasters upon detecting distribution shifts, and an elimination policy to prune obsolete models under memory constraints. Experiments on real-world datasets demonstrate that CEP significantly outperforms state-of-the-art baselines, reducing forecasting error by up to 24% on datasets with pronounced recurring drift without accessing historical ground truth.