SemTS: Ontology and Vocabularies for the Semantic Categorization of Time Series Knowledge

Although time series analytics plays an important role across diverse application domains, efficiently managing the resulting insights remains a significant challenge. While specialized ontologies structure information of analytical models, they provide limited support for standardizing and arranging inferred knowledge. The absence of a unified data model to categorize findings such as anomalies, trends, or patterns complicates reuse and inhibits synergy effects between subsequent utilization stages. This paper presents the Semantic Time Series Ontology - SemTS, an ontology designed to classify time series characteristics as explicit knowledge entities, facilitating their consistent and semantic representation. The associated integration of related concepts through specially created vocabularies improves the dissemination of insights across various abstraction levels. SemTS further enables the description and incorporation of scenario-specific information, including domain expertise, to enrich analytical contexts. To demonstrate its practical utility, we showcase various aspects of SemTS through competency questions that illustrate how the ontology can be employed to efficiently query and validate semantic time series information. By systematically combining inferred knowledge and predefined facts, SemTS provides a comprehensive framework for improving the reusability and integration of insights affiliated with time series data.