001461386 000__ 03935cam\a2200613\i\4500
001461386 001__ 1461386
001461386 003__ OCoLC
001461386 005__ 20230503003350.0
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001461386 007__ cr\cn\nnnunnun
001461386 008__ 230314s2023\\\\sz\a\\\\ob\\\\000\0\eng\d
001461386 019__ $$a1372549208
001461386 020__ $$a9783031168918$$q(electronic bk.)
001461386 020__ $$a3031168917$$q(electronic bk.)
001461386 020__ $$z9783031168901
001461386 020__ $$z3031168909
001461386 0247_ $$a10.1007/978-3-031-16891-8$$2doi
001461386 035__ $$aSP(OCoLC)1372557458
001461386 040__ $$aGW5XE$$beng$$erda$$epn$$cGW5XE$$dYDX$$dEBLCP
001461386 049__ $$aISEA
001461386 050_4 $$aHA30.3
001461386 08204 $$a519.5/5$$223/eng/20230314
001461386 1001_ $$aPrivalʹskiĭ, V. E.$$q(Viktor Evseevich),$$eauthor.$$1https://isni.org/isni/0000000026617376
001461386 24510 $$aPractical time series analysis in natural sciences /$$cVictor Privalsky.
001461386 264_1 $$aCham :$$bSpringer,$$c[2023]
001461386 264_4 $$c©2023
001461386 300__ $$a1 online resource (xi, 199 pages) :$$billustrations.
001461386 336__ $$atext$$btxt$$2rdacontent
001461386 337__ $$acomputer$$bc$$2rdamedia
001461386 338__ $$aonline resource$$bcr$$2rdacarrier
001461386 4901_ $$aProgress in geophysics
001461386 504__ $$aIncludes bibliographical references.
001461386 5050_ $$aChapter 1. Introduction -- Chapter 2. Scalar time series -- Chapter 3. Bivariate time series analysis -- Chapter 4. Analysis of trivariate time series -- Chapter 5. Conclusions and recommendations.
001461386 506__ $$aAccess limited to authorized users.
001461386 520__ $$aThis book presents an easy-to-use tool for time series analysis and allows the user to concentrate upon studying time series properties rather than upon how to calculate the necessary estimates. The two attached programs provide, in one run of the program, a time and frequency domain description of scalar or multivariate time series approximated with a sequence of autoregressive models of increasing orders. The optimal orders are chosen by five order selection criteria. The results for scalar time series include time domain stochastic difference equations, spectral density estimates, predictability properties, and a forecast of scalar time series based upon the Kolmogorov-Wiener theory. For the bivariate and trivariate time series, the results contain a time domain description with multivariate stochastic difference equations, statistical predictability criterion, and information for calculating feedback and Granger causality properties in the bivariate case. The frequency domain information includes spectral densities, ordinary, multiple, and partial coherence functions, ordinary and multiple coherent spectra, gain, phase, and time lag factors. The programs seem to be unique and using them does not require professional knowledge of theory of random processes. The book contains many examples including three from engineering.
001461386 588__ $$aDescription based on print version record.
001461386 650_0 $$aTime-series analysis$$xComputer programs.
001461386 655_0 $$aElectronic books.
001461386 77608 $$iPrint version:$$aPrivalʹskiĭ, V. E. (Viktor Evseevich).$$tPractical time series analysis in natural sciences.$$dCham : Springer, 2022$$z9783031168901$$w(OCoLC)1348393103
001461386 830_0 $$aProgress in geophysics.
001461386 852__ $$bebk
001461386 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-16891-8$$zOnline Access$$91397441.1
001461386 909CO $$ooai:library.usi.edu:1461386$$pGLOBAL_SET
001461386 980__ $$aBIB
001461386 980__ $$aEBOOK
001461386 982__ $$aEbook
001461386 983__ $$aOnline
001461386 994__ $$a92$$bISE