This article proposes knowledge-based short-time prediction methods for multivariate streaming time series, relying on the early recognition of local patterns. A parametric, well-interpretable model for such patterns is presented, along with an online, classification-based recognition procedure. Subsequently, two options are discussed to predict time series employing the fuzzified pattern knowledge, accompanied by an example. Special emphasis is placed on comprehensible models and methods, as well as an easy interface to data mining algorithms.
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa.de:bsz:ch1-201001012 |
| Date | 05 July 2010 |
| Creators | Herbst, Gernot |
| Contributors | TU Chemnitz, Fakultät für Elektrotechnik und Informationstechnik, Springer-Verlag Berlin Heidelberg, |
| Publisher | Universitätsbibliothek Chemnitz |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | doc-type:conferenceObject |
| Format | application/pdf, text/plain, application/zip |
| Source | Proceedings of the 13th International Conference on Information Processing and Management of Uncertainty (IPMU 2010), Dortmund, Germany, June 28-July 2, 2010. |
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