Time-series segmentation: A model and a method. Sclove, S. L. Information Sciences, 29(1):7–25, February, 1983. ![Time-series segmentation: A model and a method [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex The problem of partitioning time series into segments is treated. The segments are considered as falling into classes. A different probability distribution is associated with each class of segment. Parametric families of distributions are considered, a set of parameter values being associated with each class. With each observation is associated an unobservable label, indicating from which class the observation arose. The label process is modeled as a Markov chain. Segmentation algorithms are obtained by applying a relaxation method to maximize the resulting likelihood function. Special attention is given to the situation in which the observations are conditionally independent, given the labels. A numerical example, segmentation of the U.S. gross national product, is given. Choice of the number of classes, using statistical model selection criteria, is illustrated.
@article{sclove_time-series_1983,
series = {Institute of {Electrical} and {Electronics} {Engineers} {Workshop} "{Applied} {Time} {Series} {Analysis}"},
title = {Time-series segmentation: {A} model and a method},
volume = {29},
issn = {0020-0255},
shorttitle = {Time-series segmentation},
url = {https://www.sciencedirect.com/science/article/pii/0020025583900075},
doi = {10.1016/0020-0255(83)90007-5},
abstract = {The problem of partitioning time series into segments is treated. The segments are considered as falling into classes. A different probability distribution is associated with each class of segment. Parametric families of distributions are considered, a set of parameter values being associated with each class. With each observation is associated an unobservable label, indicating from which class the observation arose. The label process is modeled as a Markov chain. Segmentation algorithms are obtained by applying a relaxation method to maximize the resulting likelihood function. Special attention is given to the situation in which the observations are conditionally independent, given the labels. A numerical example, segmentation of the U.S. gross national product, is given. Choice of the number of classes, using statistical model selection criteria, is illustrated.},
number = {1},
urldate = {2024-05-06},
journal = {Information Sciences},
author = {Sclove, Stanley L.},
month = feb,
year = {1983},
keywords = {time-series segmentation},
pages = {7--25},
}
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