More and more data is gathered every day and time series are a major part of it. Due to the usefulness of this type of data, it is analyzed in many application domains. While there already exists a broad variety of methods for this task, there is still a lack of approaches that address new requirements brought up by large-scale time series data like cross-domain usage or compensation of missing data. In this paper, we address these issues, by presenting novel approaches for generating and forecasting large-scale time series data.
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:86040 |
| Date | 16 June 2023 |
| Creators | Hahmann, Martin, Hartmann, Claudio, Kegel, Lars, Lehner, Wolfgang |
| Publisher | Springer |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/acceptedVersion, doc-type:article, info:eu-repo/semantics/article, doc-type:Text |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | 1610-1995, 10.1007/s13222-018-00304-5 |
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