针对时间序列概念漂移，提出动态多周期专家框架，区分重现与新生漂移并分别应对。
Dynamic Multi-period Experts for Online Time Series Forecasting
- 将概念漂移分为重现与新生两类，构建针对性应对机制。
- 在重现漂移中动态调用历史周期专家，在新生漂移中切换至稳定通用专家。
- 在多个基准数据集上显著优于现有方法，适合在线预测场景。
在线时间序列预测（OTSF）要求模型持续适应概念漂移。然而，现有方法常将概念漂移视为单一现象。为此，我们首先通过分类重新定义概念漂移：重复性漂移（此前出现过的模式再次出现）和突发性漂移（完全新的模式出现）。随后提出DynaME（动态多周期专家）框架，以有效应对漂移的双重特性。对于重复性漂移，DynaME在每个时间步动态拟合最相关的过去周期模式，使用一组专业化专家组成委员会；对于突发性漂移，框架检测高不确定性情境，并将依赖转移至一个稳定的通用专家。在多个基准数据集和骨干模型上的大量实验表明，DynaME能有效适应两类概念漂移，显著优于现有基线方法。
原文摘要 · Abstract (English)
Online Time Series Forecasting (OTSF) requires models to continuously adapt to concept drift. However, existing methods often treat concept drift as a monolithic phenomenon. To address this limitation, we first redefine concept drift by categorizing it into two distinct types: Recurring Drift, where previously seen patterns reappear, and Emergent Drift, where entirely new patterns emerge. We then propose DynaME (Dynamic Multi-period Experts), a novel hybrid framework designed to effectively address this dual nature of drift. For Recurring Drift, DynaME employs a committee of specialized experts that are dynamically fitted to the most relevant historical periodic patterns at each time step. For Emergent Drift, the framework detects high-uncertainty scenarios and shifts reliance to a stable, general expert. Extensive experiments on several benchmark datasets and backbones demonstrate that DynaME effectively adapts to both concept drifts and significantly outperforms existing baselines.
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