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.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for detecting an anomaly in real time series, comprising the steps of: determining a similarity matrix of nonnegative pairwise similarities between pairs of normal time series data; applying a spectral clustering procedure to the similarity matrix to partition variables representing dimensions of the time series data into groups, wherein the groups are mutually exclusive; estimating a model of normal behavior for each group; determining, for the real time series data, an anomaly score using the model of normal behavior for each group; and comparing the anomaly score to a predetermined threshold to signal the anomaly, wherein the steps are performed in a processor. 2. The method of claim 1, therein the real time series data are acquired by sensor. 3. The method of claim 1, wherein the spectral clustering uses an absolute value of a correlation coefficient as a similarity measure. 4. The method of claim 3, wherein the correlation coefficient is linear, non-linear, an output of a radial-basis function network, or a support vector machine. 5. The method of claim 1, wherein the spectral clustering maximizes a similarity between variables in the same group, and minimizes the similarity between elements in different groups. 6. The method of claim 1, wherein the dimensions are of an M-dimensional Euclidean space. 7. The method of claim 1, wherein the spectral clustering uses a normalized cats procedure. 8. The method of claim 3, wherein the similarity measure bij between pairs of variables i and j is not necessarily symmetric, and the similarity matrix is A with aij=(bij+bji)/2. 9. The method of claim 1, wherein the model of each group is represented by a probability density function Over the variables in the group. 10. The method of claim 9, wherein the probability density function is a multivariate Gaussian distribution. 11. The method of claim 9. wherein the probability density function is a non-parametric Parzen kernel density estimate. 12. The method of claim 1, further comprising: combining the anomaly scores to form a global anomaly score; and comparing the global anomaly score to the predetermined threshold to signal the anomaly. 13. The method of claim 12, wherein the combining is a weighted addition, with weights proportional to a number of variables of each group. 14. The method of claim 1, wherein combining uses a general logical expression. 15. The method of claim 14, wherein the logical expression is composed by means of logical operators AND and OR over logical conditions on the scores against group-specific thresholds.