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

TL;DR

The paper introduces Continuous Evolution Pool (CEP), a privacy-preserving framework for online time series forecasting that effectively manages recurring concept drift without storing raw data, outperforming existing methods.

Contribution

CEP is a novel framework that maintains a dynamic pool of specialized forecasters using lightweight statistical genes, enabling privacy-preserving handling of concept drift in real-time forecasting.

Findings

CEP reduces forecasting error by over 20% compared to baselines.

CEP effectively detects and adapts to distribution shifts.

CEP operates under strict privacy constraints without accessing historical ground truth.

Abstract

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.