Short-Time Prediction Based on Recognition of Fuzzy Time Series Patterns
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. Sub...
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| Format: | Others |
| Language: | English |
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Universitätsbibliothek Chemnitz
2010
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| Online Access: | http://nbn-resolving.de/urn:nbn:de:bsz:ch1-201001012 http://nbn-resolving.de/urn:nbn:de:bsz:ch1-201001012 http://www.qucosa.de/fileadmin/data/qucosa/documents/6025/data/GernotHerbst_Ipmu2010.pdf http://www.qucosa.de/fileadmin/data/qucosa/documents/6025/20100101.txt |
| Summary: | 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. |
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