Change Points Detection of Vector Autoregressive Model using SDVAR Algorithm

Part of a larger research project to detect fraudulent acts using the telecommunications call details record (CDR) is to locate the change points which could lead to detecting suspicious (fraudulent) calls. The capability of sequential discounting for autoregressive (SDAR) model learning algorithm (as proposed by [6]) to detect change points in time series data is explored. The algorithm is extended to multivariate time series by employing vector autoregressive model using SDVAR. Simulation and real data experiments to illustrate the new algorithm are discussed in this paper.