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Time series analysis in climatology ...

Privalsky, Victor.

Time series analysis in climatology and related sciences

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Time series analysis in climatology and related sciencesby Victor Privalsky.
作者:	Privalsky, Victor.
出版者:	Cham :Springer International Publishing :2021.
面頁冊數:	xiv, 245 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Time-series analysis.
電子資源:	https://doi.org/10.1007/978-3-030-58055-1
ISBN:	9783030580551$q(electronic bk.)

Time series analysis in climatology and related sciences
Privalsky, Victor.

Time series analysis in climatology and related sciences[electronic resource] /by Victor Privalsky. - Cham :Springer International Publishing :2021. - xiv, 245 p. :ill., digital ;24 cm. - Progress in geophysics,2523-8388. - Progress in geophysics..

Chapter 1 - Introduction -- Chapter 2 - Basics of Scalar Random Processes -- Chapter 3 - Time and Frequency Domain Models of Scalar Time Series -- Chapter 4 - Practical Analysis of Time Series -- Chapter 5 - Stochastic models and spectra of climatic and related time series -- Chapter 6 - Statistical Forecasting of Geophysical Processes -- Chapter 7 - Bivariate Time Series Analysis -- Chapter 8 - Teleconnection Research and Bivariate Extrapolation -- Chapter 9 - Reconstruction of Time Series -- Chapter 10 - Frequency domain structure and feedbacks in QBO time series -- Chapter 11- Verification of General Circulation Models -- Chapter 12 - Applications to proxy data -- Chapter 13 - Application to Sunspot Numbers and Total Solar Irradiance -- Chapter 14 - Multivariate time and frequency domain analysis -- Chapter 15 - Summary and Recommendations.

This book gives the reader the basic knowledge of the theory of random processes necessary for applying to study climatic time series. It contains many examples in different areas of time series analysis such as autoregressive modelling and spectral analysis, linear extrapolation, simulation, causality, relations between scalar components of multivariate time series, and reconstructions of climate data. As an important feature, the book contains many practical examples and recommendations about how to deal and how not to deal with applied problems of time series analysis in climatology or any other science where the time series are short.

ISBN: 9783030580551$q(electronic bk.)

Standard No.: 10.1007/978-3-030-58055-1doiSubjects--Topical Terms:

181890
Time-series analysis.

LC Class. No.: QA280 / .P75 2021

Dewey Class. No.: 519.55

Time series analysis in climatology and related sciences
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505 0 $a Chapter 1 - Introduction -- Chapter 2 - Basics of Scalar Random Processes -- Chapter 3 - Time and Frequency Domain Models of Scalar Time Series -- Chapter 4 - Practical Analysis of Time Series -- Chapter 5 - Stochastic models and spectra of climatic and related time series -- Chapter 6 - Statistical Forecasting of Geophysical Processes -- Chapter 7 - Bivariate Time Series Analysis -- Chapter 8 - Teleconnection Research and Bivariate Extrapolation -- Chapter 9 - Reconstruction of Time Series -- Chapter 10 - Frequency domain structure and feedbacks in QBO time series -- Chapter 11- Verification of General Circulation Models -- Chapter 12 - Applications to proxy data -- Chapter 13 - Application to Sunspot Numbers and Total Solar Irradiance -- Chapter 14 - Multivariate time and frequency domain analysis -- Chapter 15 - Summary and Recommendations.
520 $a This book gives the reader the basic knowledge of the theory of random processes necessary for applying to study climatic time series. It contains many examples in different areas of time series analysis such as autoregressive modelling and spectral analysis, linear extrapolation, simulation, causality, relations between scalar components of multivariate time series, and reconstructions of climate data. As an important feature, the book contains many practical examples and recommendations about how to deal and how not to deal with applied problems of time series analysis in climatology or any other science where the time series are short.
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