Method for anomaly detection in time series data based on spectral partitioning
US9984334B2 · kind B2 · utility
7Cited by
2References
19Claims
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Key dates
| Filing date | Jun 16, 2014 |
| Grant date | May 29, 2018 |
| Priority date | — |
| Expiry date | Dec 20, 2036 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N20/00
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Anomalies in real time series are detected by first determining a similarity matrix of pairwise similarities between pairs of normal time series data. A spectral clustering procedure is applied to the similarity matrix to partition variables representing dimensions of the time series data into mutually exclusive groups. A model of normal behavior is estimated for each group. Then, for the real time series data, an anomaly score is determined, using the model for each group, and the anomaly score is compared to a predetermined threshold to signal the anomaly.
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