On-line change-point detection (for state space models) using multi-process Kalman filters
Based on multi-process Kalman filters, an algorithm is presented for estimating the probability that a change-point has occurred in a noisy time series. Especially, change-points are considered which arise in biomedical signals, i.e. jumps or drifts in nonstationary time series possibly corrupted by embedded outliers. The stability of the algorithm against outliers is examined, and an example is given.
scientific article; zbMATH DE number 1371016