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 perform segmentation satisfactorily and fast.