Difference between revisions of "Finding patterns/motifs in time series data"
From ISLAB/CAISR
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|Summary=Finding patterns/motifs in time series data, for autonomous clustering or outlier detection | |Summary=Finding patterns/motifs in time series data, for autonomous clustering or outlier detection | ||
| − | |Supervisor=Thorsteinn Rögnvaldsson, Mohamed-Rafik Bouguelia, | + | |Supervisor=Thorsteinn Rögnvaldsson, Mohamed-Rafik Bouguelia, |
| + | |Author=Felix Nilsson | ||
|Level=Master | |Level=Master | ||
| − | |Status= | + | |Status=Finished |
}} | }} | ||
The goal of this project is to find patterns/motifs in time series data. This can be applied for the purpose of autonomous clustering (to explore the data and better understand how a system works) or for the purpose of outlier detection (e.g. to detect faults in a given system). | The goal of this project is to find patterns/motifs in time series data. This can be applied for the purpose of autonomous clustering (to explore the data and better understand how a system works) or for the purpose of outlier detection (e.g. to detect faults in a given system). | ||
Latest revision as of 16:32, 20 September 2021
| Title | Finding patterns/motifs in time series data |
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| Summary | Finding patterns/motifs in time series data, for autonomous clustering or outlier detection |
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| Author | Felix Nilsson |
| Supervisor | Thorsteinn Rögnvaldsson, Mohamed-Rafik Bouguelia |
| Level | Master |
| Status | Finished |
The goal of this project is to find patterns/motifs in time series data. This can be applied for the purpose of autonomous clustering (to explore the data and better understand how a system works) or for the purpose of outlier detection (e.g. to detect faults in a given system).
