Fast segmentation algorithms for long hydrometeorological time series

A time series with natural or artificially created inhomogeneities can be segmented into parts with different statistical characteristics. In this study, three algorithms are presented for time series segmentation; the first is based on dynamic programming and the second and the third-the latter being an improved version of the former-are based on the branch-and-bound approach. The algorithms divide the time series into segments using the first order statistical moment (average). Tested on real world time series of several hundred or even over a thousand terms the algorithms performs segmentations satisfactorily and fast. (c) Copyright 2008 John Wiley & Sons, Ltd.