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Practical time series analysis in natural sciences

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Practical time series analysis in natural sciencesby Victor Privalsky.
作者:	Privalsky, Victor.
出版者:	Cham :Springer International Publishing :2023.
面頁冊數:	xi, 199 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Time-series analysisComputer programs.
電子資源:	https://doi.org/10.1007/978-3-031-16891-8
ISBN:	9783031168918$q(electronic bk.)

Practical time series analysis in natural sciences
Privalsky, Victor.

Practical time series analysis in natural sciences[electronic resource] /by Victor Privalsky. - Cham :Springer International Publishing :2023. - xi, 199 p. :ill., digital ;24 cm. - Progress in geophysics,2523-8396. - Progress in geophysics..

Chapter 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.

This 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.

ISBN: 9783031168918$q(electronic bk.)

Standard No.: 10.1007/978-3-031-16891-8doiSubjects--Topical Terms:

247656
Time-series analysis
--Computer programs.

LC Class. No.: HA30.3

Dewey Class. No.: 519.55

Practical time series analysis in natural sciences
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520 $a This 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.
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