Time-series novelty detection using one-class support vector machines

Time-series novelty detection, or anomaly detection, refers to the automatic identification of novel or abnormal events embedded in normal time-series points. Although it is a challenging topic in data mining, it has been acquiring increasing attention due to its huge potential for immediate applications. In this paper, a new algorithm for time-series novelty detection based on one-class support vector machines (SVMs) is proposed. The concepts of phase and projected phase spaces are first introduced, which allows us to convert a time-series into a set of vectors in the (projected) phase spaces. Then we interpret novel events in time-series as outliers of the "normal" distribution of the converted vectors in the (projected) phase spaces. One-class SVMs are employed as the outlier detectors. In order to obtain robust detection results, a technique to combine intermediate results at different phase spaces is also proposed. Experiments on both synthetic and measured data are presented to demonstrate the promising performance of the new algorithm.