Adjusting Monitored Experiments to Real-World Cases by Matching Labeled Time Series Motifs
| Type of publication: | Inproceedings |
| Citation: | moewes_adjustung_2008 |
| Booktitle: | Proceedings 18. Workshop Computational Intelligence, Dortmund, 3. - 5. Dezember 2008 |
| Series: | Schriftenreihe des IAI, Universität Karlsruhe (TH) |
| Year: | 2008 |
| Month: | December |
| Pages: | 214--223 |
| Publisher: | Universitätsverlag Karlsruhe |
| Address: | Karlsruhe, Germany |
| ISBN: | 978-3-86644-282-5 |
| URL: | http://digbib.ubka.uni-karlsru... |
| DOI: | 10.5445/KSP/1000009271 |
| Abstract: | In this paper we devote ourselves to the difficulty of fitting human designed experiments to real-world cases. We decompose this problem into two smaller subproblems: 1.) The search of recurrent patterns in temporal sequences, so called motifs that are deemed to be discovered in both the experiments and the real observations and 2.) the matching of motifs to linguistic terms which are possibly available as domain knowledge. Therefore we describe an effective time series representation that enormously speeds up the search for these motifs. We present some approaches to adjust the designed experiments with the help of the discovered motifs. Finally, we conclude our work and give prospects to possible extensions. |
| Keywords: | Frequent Pattern Mining, Labeling, Motif Discovery, Multivariate Time Series Analysis |
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| Added by: | [ADM] |
| Total mark: | 0 |
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