An adaptive multi-level correlation analysis: A new algorithm and case study
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AbstractAn adaptive multilevel correlation analysis, a kind of data-driven methodology, is proposed. The analysis is done by subdividing the time series into segments such that adjacent segments have significantly different mean values. It is shown that the proposed methodology can provide multilevel information about the correlation between two variables. An integrated coefficient with its significance testing is also proposed to summarize the correlation at each level. Using the adaptive multilevel correlation analysis methodology, the correlation between streamflow and water level is investigated for a case study, and the results indicate that real correlation might be far more complicated than the empirically constructed picture.
All Author(s) ListYu Zhou, Qiang Zhang, Vijay P. Singh
Journal nameHydrological Sciences Journal
Year2016
Volume Number61
Issue Number15
PublisherTaylor & Francis
Pages2718 - 2728
ISSN0262-6667
eISSN2150-3435
LanguagesEnglish-United States
KeywordsCorrelation analysis, adaptive segmentation, local correlation analysis, multilevel analysis

Last updated on 2020-18-10 at 02:12