Autonomous Drift Learning in Data Streams: A Unified Perspective

TL;DR

This paper introduces a comprehensive taxonomy for autonomous data stream learning, addressing various types of drift and system evolution to guide the development of self-adapting intelligent systems.

Contribution

It proposes a novel three-dimensional taxonomy for classifying different types of drift and system changes, unifying diverse research paradigms in autonomous data stream learning.

Findings

Systematic review of 193 studies using the taxonomy

Identification of key open challenges in autonomous drift learning

Bridging paradigms of drift adaptation, continual learning, and temporal generalization

Abstract

In the pursuit of autonomous learning systems, the foundational assumption of stationarity, the premise that data distributions and model behaviors remain constant, is fundamentally untenable. Historically, the research community has addressed non-stationary environments almost exclusively under the scope of concept drift, focusing primarily on temporal shifts in streams. However, as learning systems become increasingly autonomous and complex, merely adapting to temporal non-stationarity is no longer sufficient. Evolving beyond this traditional perspective, we propose a novel, three-dimensional taxonomy that systematizes the field based on the operational state of the system. First, time stream drift distinguishes between stochastic arbitrary patterns and structural rhythmic dynamics. Second, data stream drift disentangles shifts in feature representations, identified as representation drift, from changes in underlying semantics, recognized as semantic drift. Third, model stream drift characterizes the internal endogenous divergence of learning systems through the lenses of sequential plasticity, decentralized heterogeneity, and policy instability. Based on this framework, we systematically review 193 representative studies and identify key open challenges. By bridging the fragmented paradigms of drift adaptation, continual learning, and temporal generalization, this survey outlines a roadmap for building self-evolving intelligent systems capable of learning autonomously through continuous change.