LAUSR

Abstract We consider the problem of incrementally segmenting auto-regressive models with exogenous inputs (ARX models) when the data is received sequentially at run-time. In particular, we extend a recently proposed dynamic programming based polynomial-time algorithm for offline (batch) ARX model segmentation to the incremental setting. The new algorithm enables sequential updating of the models, eliminating repeated computation, while remaining optimal. We also show how certain noise bounds can be used to detect switches automatically at run-time. The efficiency of the approach compared to the batch method is illustrated on synthetic and real data.