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Record Nr. |
UNINA9910299405603321 |
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Autore |
Zhang Zhihua |
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Titolo |
Multivariate Time Series Analysis in Climate and Environmental Research / / by Zhihua Zhang |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 |
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ISBN |
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Edizione |
[1st ed. 2018.] |
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Descrizione fisica |
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1 online resource (XII, 287 p. 7 illus., 3 illus. in color.) |
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Disciplina |
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Soggetti |
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Climatic changes |
Physical geography |
Oceanography |
Ecology |
Statistics |
Climate Change |
Physical Geography |
Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references at the end of each chapters. |
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Nota di contenuto |
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Multivariate Harmonic Analysis -- Multivariate Wavelets and Framelits -- Artificial Neural Networks -- Stochastic Modeling and Optimization -- Spectral Analysis -- Global Climate Change -- Regional Climate Change -- Ecosystem and Carbon Cycle -- Paleoclimate -- Strategies for climate change mitigation. |
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Sommario/riassunto |
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This book offers comprehensive information on the theory, models and algorithms involved in state-of-the-art multivariate time series analysis and highlights several of the latest research advances in climate and environmental science. The main topics addressed include Multivariate Time-Frequency Analysis, Artificial Neural Networks, Stochastic Modeling and Optimization, Spectral Analysis, Global Climate Change, Regional Climate Change, Ecosystem and Carbon Cycle, Paleoclimate, and Strategies for Climate Change Mitigation. The self-contained guide will be of great value to researchers and advanced students from a wide |
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range of disciplines: those from Meteorology, Climatology, Oceanography, the Earth Sciences and Environmental Science will be introduced to various advanced tools for analyzing multivariate data, greatly facilitating their research, while those from Applied Mathematics, Statistics, Physics, and the Computer Sciences will learn how to use these multivariate time series analysis tools to approach climate and environmental topics. . |
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