Name
Abstract | ||
|---|---|---|
This article presents SwiftMotif, a novel technique for on-line motif detection in time series. With this technique, frequently occurring temporal patterns or anomalies can be discovered, for instance. The motif detection is based on a fusion of methods from two worlds: probabilistic modeling and similarity measurement techniques are combined with extremely fast polynomial least-squares approximation techniques. A time series is segmented with a data stream segmentation method, the segments are modeled by means of normal distributions with time-dependent means and constant variances, and these models are compared using a divergence measure for probability densities. Then, using suitable clustering algorithms based on these similarity measures, motifs may be defined. The fast time series segmentation and modeling techniques then allow for an on-line detection of previously defined motifs in new time series with very low run-times. SwiftMotif is suitable for real-time applications, accounts for the uncertainty associated with the occurrence of certain motifs, e.g., due to noise, and considers local variability (i.e., uniform scaling) in the time domain. This article focuses on the mathematical foundations and the demonstration of properties of SwiftMotif-in particular accuracy and run-time-using some artificial and real benchmark time series. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1016/j.patcog.2009.05.004 | Pattern Recognition |
Keywords | Field | DocType |
time domain,time series,temporal data mining,piecewise polynomial representation,swiftmotif,new time series,probabilistic modeling,piecewise probabilistic representation,fast time series segmentation,real benchmark time series,on-line detection,motif detection,on-line motif detection,approximation technique,segmentation,polynomial approximation,orthogonal polynomials,certain motif,probability density,probabilistic model,least squares approximation,orthogonal polynomial,normal distribution | Time domain,Similitude,Normal distribution,Time-series segmentation,Pattern recognition,Polynomial,Segmentation,Algorithm,Artificial intelligence,Probabilistic logic,Cluster analysis,Mathematics | Journal |
Volume | Issue | ISSN |
42 | 11 | Pattern Recognition |
Citations | PageRank | References |
24 | 1.28 | 25 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Erich Fuchs | 1 | 42 | 3.45 |
| Thiemo Gruber | 2 | 94 | 6.37 |
| Jiri Nitschke | 3 | 24 | 1.28 |
| Bernhard Sick | 4 | 704 | 70.42 |