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010   $a3-319-67340-8
024 7 $a10.1007/978-3-319-67340-0
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035   $a(DE-He213)978-3-319-67340-0
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035   $a(PPN)221254951
035   $a(EXLCZ)994100000001040704
100   $a20171110d2018      u|         0
101 0 $aeng
135   $aurnn|008mamaa
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aMultivariate Time Series Analysis in Climate and Environmental Research /$fby Zhihua Zhang
205   $a1st ed. 2018.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2018.
215   $a1 online resource (XII, 287 p. 7 illus., 3 illus. in color.) 
311   $a3-319-67339-4 
320   $aIncludes bibliographical references at the end of each chapters.
327   $aMultivariate 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.
330   $aThis 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 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.  .
606   $aClimatic changes
606   $aPhysical geography
606   $aOceanography
606   $aEcology
606   $aStatistics
606   $aClimate Change$3https://scigraph.springernature.com/ontologies/product-market-codes/U12007
606   $aPhysical Geography$3https://scigraph.springernature.com/ontologies/product-market-codes/J16000
606   $aOceanography$3https://scigraph.springernature.com/ontologies/product-market-codes/G25005
606   $aEcology$3https://scigraph.springernature.com/ontologies/product-market-codes/L19007
606   $aStatistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences$3https://scigraph.springernature.com/ontologies/product-market-codes/S17020
615  0$aClimatic changes.
615  0$aPhysical geography.
615  0$aOceanography.
615  0$aEcology.
615  0$aStatistics.
615 14$aClimate Change.
615 24$aPhysical Geography.
615 24$aOceanography.
615 24$aEcology.
615 24$aStatistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
676   $a577.27
700   $aZhang$b Zhihua$4aut$4http://id.loc.gov/vocabulary/relators/aut$01031422
906   $aBOOK
912   $a9910299405603321
996   $aMultivariate Time Series Analysis in Climate and Environmental Research$92520667
997   $aUNINA