Change Point Detection in Time Series Data Using Support Vectors

Change Point Detection in time series data is of interest in various research areas including data mining, pattern recognition, statistics, etc. Even though there are several e®ective methods in the literature for detecting changes in mean, and an increase in variance, there are none for decrease in variance. E®ective detection of decreased variance has been reported as future work in earlier papers. In addition, most, if not all, methods require some model like AR to ̄t into the time series data in order to extract noise information, which is assumed to be independent and identically distributed (i.i.d.) and follow standard normal distribution (white noise). Thus, e®ectiveness of the methods is tied to the ̄tness degree of the AR model to the time series data. This paper presents a change point detection method based on support vectors that targets changes in mean and variance (including variance decrease) without any assumption of model ̄tting or data distribution. The data is represented by a hyper-sphere in a higher dimensional space using kernel trick. The change is identi ̄ed by the change in the radius of the hyper-sphere. A comparison of this method with other methods is presented in the paper.