CORAL: Concept Drift Representation Learning for Co-evolving Time-series

TL;DR

CORAL is a novel method for modeling and detecting concept drift in co-evolving time series, improving adaptability and accuracy by learning dynamic representations of evolving data patterns.

Contribution

It introduces a kernel-induced self-representation learning approach to model concept drift in co-evolving time series, enabling better detection and adaptation.

Findings

Demonstrates effectiveness across various datasets

Enhances pattern identification and trend analysis

Integrates easily with deep learning models

Abstract

In the realm of time series analysis, tackling the phenomenon of concept drift poses a significant challenge. Concept drift -- characterized by the evolving statistical properties of time series data, affects the reliability and accuracy of conventional analysis models. This is particularly evident in co-evolving scenarios where interactions among variables are crucial. This paper presents CORAL, a simple yet effective method that models time series as an evolving ecosystem to learn representations of concept drift. CORAL employs a kernel-induced self-representation learning to generate a representation matrix, encapsulating the inherent dynamics of co-evolving time series. This matrix serves as a key tool for identification and adaptation to concept drift by observing its temporal variations. Furthermore, CORAL effectively identifies prevailing patterns and offers insights into emerging trends through pattern evolution analysis. Our empirical evaluation of CORAL across various datasets demonstrates its effectiveness in handling the complexities of concept drift. This approach introduces a novel perspective in the theoretical domain of co-evolving time series analysis, enhancing adaptability and accuracy in the face of dynamic data environments, and can be easily integrated into most deep learning backbones.