Recurring concept drift poses a dual challenge in online time series forecasting: mitigating catastrophic forgetting while adhering to strict privacy constraints that prevent retaining historical data. Existing approaches predominantly rely on parameter updates or experience replay, which inevitably suffer from knowledge overwriting or privacy risks. To address this, we propose the Continuous Evolution Pool (CEP), a privacy-preserving framework that maintains a dynamic pool of specialized forecasters. 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 over 20% without accessing historical ground truth.

翻译：周期性概念漂移为在线时间序列预测带来了双重挑战：既要缓解灾难性遗忘，又需遵循严格的隐私约束——即禁止保留历史数据。现有方法主要依赖参数更新或经验回放，这不可避免地面临知识覆盖或隐私风险。为解决这一问题，我们提出了连续进化池（CEP），一种隐私保护框架，通过维护一个动态的专业预测器池来实现。CEP不存储原始样本，而是利用轻量级统计基因将概念识别与预测解耦。具体而言，它采用检索机制基于基因相似度识别最近邻概念，通过进化策略在检测到分布漂移时生成新预测器，并利用淘汰策略在内存限制下修剪过时模型。在真实数据集上的实验表明，CEP显著优于现有先进基线，在不访问历史真实值的情况下将预测误差降低了20%以上。