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Feature-based Comparison and Generation of Time Series

For more than three decades, researchers have been developping generation methods for the weather, energy, and economic domain. These methods provide generated datasets for reasons like system evaluation and data availability. However, despite the variety of approaches, there is no comparative and cross-domain assessment of generation methods and their expressiveness. We present a similarity measure that analyzes generation methods regarding general time series features. By this means, users can compare generation methods and validate whether a generated dataset is considered similar to a given dataset. Moreover, we propose a feature-based generation method that evolves cross-domain time series datasets. This method outperforms other generation methods regarding the feature-based similarity.

Identiferoai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:80440
Date17 August 2022
CreatorsKegel, Lars, Hahmann, Martin, Lehner, Wolfgang
PublisherACM
Source SetsHochschulschriftenserver (HSSS) der SLUB Dresden
LanguageEnglish
Detected LanguageEnglish
Typeinfo:eu-repo/semantics/acceptedVersion, doc-type:conferenceObject, info:eu-repo/semantics/conferenceObject, doc-type:Text
Rightsinfo:eu-repo/semantics/openAccess
Relation978-1-4503-6505-5, 20, 10.1145/3221269.3221293, info:eu-repo/grantAgreement/European Commission/H2020 | IA/731232//Generalized Operational FLEXibility for Integrating Renewables in the Distribution Grid/GOFLEX

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