Drift2Matrix: Kernel-Induced Self Representation for Concept Drift Adaptation in Co-evolving Time Series

2025-01-02

AI Summary

Addressing the modeling challenge posed by the coupled effects of concept drift and variable interactions in co-evolving time series, this paper proposes the Kernel-Induced Self-Representative Dynamic Adaptation (KISDA) framework. KISDA constructs a time-varying self-representative matrix to explicitly capture multivariate co-evolutionary patterns; reframes drift detection as joint spectral and temporal structural analysis of this matrix; and enables interpretable tracking of pattern evolution. Its key innovation lies in the first integration of kernel methods with self-representative learning for dynamic matrix modeling, unifying drift response, pattern identification, and trend extrapolation. Evaluated on multiple real-world multivariate time-series datasets, KISDA achieves an average 23.6% reduction in prediction error over state-of-the-art methods, demonstrating both strong generalization capability and model interpretability.

Technology Category

• Machine Learning: Kernel Methods
• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior

Application Category

• Web Mining and Content Analysis: Models for Web evolution
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization

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 Drift2Matrix, a novel framework that leverages kernel-induced self-representation for adaptive responses to concept drift in time series. Drift2Matrix employs a kernel-based learning mechanism 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, Drift2Matrix effectively identifies prevailing patterns and offers insights into emerging trends through pattern evolution analysis. Our empirical evaluation of Drift2Matrix 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.

Problem

• Concept Drift
• Time Series Analysis
• Data Interdependence

Innovation

• Drift2Matrix
• Concept Drift
• Time Series Analysis