2002/06/25 by Athanasios Kehagias, Kehagias, Ath.
Environmental Science · #Chaotic Dynamics (nlin.CD) #Computational Engineering #Data Analysis #FOS: Computer and information sciences #FOS: Mathematics #FOS: Physical sciences #Finance #G.3 #Hydrological Forecasting Using AI #Hydrology and Drought Analysis #Hydrology and Watershed Management Studies #I.5 #Numerical Analysis (math.NA) #Statistics and Probability (physics.data-an) #and Science (cs.CE)
paper · pdf · doi:10.48550/arxiv.cs/0206039
openalex publication_date 2002/06/25 · openalex created_date 2016/06/24 · openalex updated_date 2026/07/28
Motivated by Hubert's segmentation procedure we discuss the application of hidden Markov models (HMM) to the segmentation of hydrological and enviromental time series. We use a HMM algorithm which segments time series of several hundred terms in a few seconds and is computationally feasible for even longer time series. The segmentation algorithm computes the Maximum Likelihood segmentation by use of an expectation / maximization iteration. We rigorously prove algorithm convergence and use numerical experiments, involving temperature and river discharge time series, to show that the algorithm usually converges to the globally optimal segmentation. The relation of the proposed algorithm to Hubert's segmentation procedure is also discussed.