| 书目名称 | Practical Time Series Analysis in Natural Sciences | | 编辑 | Victor Privalsky | | 视频video | | | 概述 | Provides a unique tool to obtain exhaustive information about statistical properties.Includes mathematically proper forecasting.Contains many examples | | 丛书名称 | Progress in Geophysics | | 图书封面 |  | | 描述 | .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 | | 出版日期 | Book 2023 | | 关键词 | Autoregressive; Stochastic Difference Equation; Time and Frequency Domain Analysis; Singular and Multiv | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-031-16891-8 | | isbn_softcover | 978-3-031-16893-2 | | isbn_ebook | 978-3-031-16891-8Series ISSN 2523-8388 Series E-ISSN 2523-8396 | | issn_series | 2523-8388 | | copyright | The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl |
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发表于 2025-3-21 17:31:04
