Authors:
Lucélia de Souza
1
;
Maria Salete Marcon Gomes Vaz
2
and
Marcos Sfair Sunye
3
Affiliations:
1
State University of Center-West and Federal University of Paraná, Brazil
;
2
Federal University of Paraná and State University of Ponta Grossa, Brazil
;
3
Federal University of Paraná, Brazil
Keyword(s):
OWL Ontology, Modular Development, DBpedia, Nonstationary Time Series, Detrending Methods.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Data Engineering
;
Enterprise Information Systems
;
Health Information Systems
;
Information Systems Analysis and Specification
;
Knowledge Engineering and Ontology Development
;
Knowledge Management
;
Knowledge-Based Systems
;
Ontologies and the Semantic Web
;
Ontology Engineering
;
Society, e-Business and e-Government
;
Symbolic Systems
;
Web Information Systems and Technologies
Abstract:
In time series analysis, the trend extraction – detrending is considered a relevant step of preprocessing, where occurs the transformation of nonstationary time series in stationary, that is, free of trends. Trends are time series components that need be removed because they can hide other phenomena, causing distortions in further processing. To helping the decision making, by researchers, about how and how often the time series were detrended, the main contribution of this paper is semantically enriching this step, presenting the Detrend Ontology (DO prefix), designed in a modular way, by reuse of ontological resources, which are extended for modeling of statistical methods applied for detrending in the time domain. The ontology was evaluated by experts and ontologists and validated by means of a case study involving real-world photometric time series. It is described its extensibility for methods in time-frequency domain, as well as the association, when applicable, of instances wi
th linked open data from DBpedia semantic knowledge base. As result of this paper, stands out the semantic enrichment of a relevant step of the analysis, contributing to the scientific knowledge generation in several areas that analyze time series.
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