Fuzzy-Based Ensemble Method for Robust Concept Drift Detection in Multivariate Time Series

Concept drift detection (CDD) is the general problem of identifying significant changes in streaming data distribution over time. Effective drift detection is important in industrial processes such as oil and gas exploration to mitigate financial losses, ensure personnel safety, and reduce environmental risks. However, current CDD methods face challenges in large-scale, multivariate datasets, where single drift detectors (DD) often fail to capture variable interdependencies. While ensemble drift detectors (EDD) are usually adopted to mitigate the adoption of a single DD, EDD may suffer when detections do not converge. This misalignment can cause voting mechanisms to neglect critical intervals with high detection rates. To address this issue, we propose a fuzzy ensemble drift detector (FEDD) that integrates unsupervised threshold voting with fuzzy logic to provide time tolerance and reconcile minor temporal misalignments in drift detection. FEDD is evaluated using the 3W dataset, a realistic public benchmark with rare undesirable real events in oil wells. The results demonstrate that FEDD outperforms existing approaches by improving detection robustness and coverage, ensuring more reliable drift detection in high-dimensional, noisy environments.