A method detects anomalies in time series data, by first learning a final set of exemplars by summarizing training time series data using a divide-and-conquer procedure. Then, for each window of testing time series data, a distance to a nearest exemplar in the final set of exemplars is determined, wherein the distance is an anomaly score. Finally, an anomaly is signaled when the anomaly score for a window is greater than a threshold.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for detecting anomalies in time series data, comprising the steps of: learning a final set of exemplars by summarizing training time series data using a divide-and-conquer procedure: determining, for each window of testing time series data, a distance to a nearest exemplar in the final set of exemplars, wherein the distance is an anomaly score; and signaling an anomaly when the anomaly score for a window is greater than a threshold, wherein the steps are performed in a processor. 2. The method of claim 1, wherein each exemplar represents a set of similar windows of the time series data. 3. The method of claim 1, wherein the divide-and conquer procedure further comprises: partitioning an initial set of exemplars in the training time series data into chunks; applying a sequence of selection procedures and combine procedures to the chunks until only one chunk remains; and applying a last selection procedure to the exemplars of the remaining chunk to produce the final set of exemplars. 4. The method of claim 3, wherein each window of the time series data is represented as a trajectory component that characterizes a shape of the time series data within the window, and a statistical component that characterizes a stochastic component, and a feature vector including the trajectory and statistical components is a statistical and smoothed trajectory (SST) feature, and wherein the windows are time-wise sliding and overlapping. 5. The method of claim 4, further comprising: determine the SSTs features for each window in the training time series data and define the initial set of exemplars equal to a set of all SST features. 6. The method of claim 1, further comprising: smoothing the time series data. 7. The method of claim 5, wherein the statistical component includes a mean, a standard deviation, a mean of an absolute difference, a number of mean crossings divided by a window size, a percentage of positive differences, a percentage of zero differences, and an average size of a sequence of positive differences divided by a size of the window. 8. The method of claim 5, wherein the selection procedure iteratively merges nearest neighbor exemplars, and where merging of the exemplars is done by computing an average of the SST features. 9. The method of claim 7, wherein the distance is dist  ( v 1 , v 2 ) = ∑ i = 1 l   ( v 1 , t  ( i ) - v 2 , t  ( i ) ) 2 + l 7  ∑ i = 1 7   ( v 1 , s  ( i ) - v 2 , s  ( i ) ) 2 , where v1 and v2 are two feature vectors, vj·t is the trajectory component of vj and vj·s is the statistical component of vj.