We propose an algorithm for simultaneously detecting and locating changepoints in a time series, and a framework for predicting the distribution of the next point in the series. The kernel of the algorithm is a system of equations that computes, for each index i, the probability that the last (most recent) change point occurred at i. We evaluate this algorithm by applying it to the change point detection problem and comparing it to the generalized likelihood ratio (GLR) algorithm. We find that our algorithm is as good as GLR, or better, over a wide range of scenarios, and that the advantage increases as the signal-to-noise ratio decreases.
Detecting changes in a data stream is an important area of research with many applications. In this paper, we present a novel method for the detection and estimation of change. In addition to providing statistical guarantees on the reliability of detected changes, our method also provides meaningful descriptions and quantification of these changes. Our approach assumes that the points in the stream are independently generated, but otherwise makes no assumptions on the nature of the generating distribution. Thus our techniques work for both continuous and discrete data. In an experimental study we demonstrate the power of our techniques.