Evolving interval-based time series clustering for streaming industrial data

Stržinar, Žiga (avtor), Škrjanc, Igor (avtor), Pregelj, Boštjan (avtor)

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.

• Jezik: Angleški jezik
• Ključne besede: evolving clustering, time series clustering, time series prototype, unsupervised learning, industrial application
• Vrsta gradiva: Članek v reviji
• Tipologija: 1.01 - Izvirni znanstveni članek
• Organizacija: FE - Fakulteta za elektrotehniko
• Status publikacije: Objavljeno
• Različica publikacije: Objavljena publikacija
• Leto izida: 2025
• Št. strani: 17 str.
• Številčenje: Vol. 16, iss. 3, art. 82
• PID: 20.500.12556/RUL-178844
• UDK: 004
• ISSN pri članku: 1868-6478
• DOI: 10.1007/s12530-025-09713-w
• COBISS.SI-ID: 240832771
• Datum objave v RUL: 30.01.2026

Gradivo je del revije

• Naslov: Evolving systems
• Skrajšan naslov: Evol. syst.
• Založnik: Springer Nature
• ISSN: 1868-6478
• COBISS.SI-ID: 8735572

Licence

• Licenca: CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
• Povezava: http://creativecommons.org/licenses/by/4.0/deed.sl
• Opis: To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.

Sekundarni jezik

• Jezik: Slovenski jezik
• Ključne besede: obdelava podatkov, strojno učenje