Evolving interval-based time series clustering for streaming industrial data

Abstract Accurate clustering of time series data is crucial for extracting meaningful insights from streaming sensor data in industrial applications. To address the challenges of dynamic and unlabeled data streams, we introduce Interval ERAL (iERAL), an enhancement of the Error in Aligned Series (ERAL) framework. iERAL is a time series alignment and averaging method designed for online analysis, incorporating an interval band to represent variance in the underlying data. We pair iERAL with an evolving time series clustering algorithm, capable of automatically detecting, adapting to, and merging clusters in real-time. This evolving approach enables the algorithm to dynamically adjust to new patterns, promote or demote clusters based on their relevance, and handle data variability with interval-based analysis. Unlike previous methods, our approach not only computes the time series prototype for each cluster but also provides a variance band for interval-based analysis. We demonstrate the effectiveness of our method by applying it to line pressure measurements in a real-world industrial setting. The algorithm achieves promising results in clustering unlabeled data streams, highlighting its potential for anomaly detection and adaptive monitoring of industrial processes in evolving operating conditions.