| 書目名稱 | Time Series Models | | 編輯 | Manfred Deistler,Wolfgang Scherrer | | 視頻video | http://file.papertrans.cn/926/925449/925449.mp4 | | 概述 | Provides an understanding of core parts of multivariate time series theory and models.Presents a self-contained exposition with numerous examples and exercises.Emphasizes weakly stationary processes a | | 叢書名稱 | Lecture Notes in Statistics | | 圖書封面 |  | | 描述 | .This textbook provides a self-contained presentation of the theory and models of time series analysis. Putting an emphasis on weakly stationary processes and linear dynamic models, it describes the basic concepts, ideas, methods and results in a mathematically well-founded form and includes numerous examples and exercises. The first part presents the theory of weakly stationary processes in time and frequency domain, including prediction and filtering. The second part deals with multivariate AR, ARMA and state space models, which are the most important model classes for stationary processes, and addresses the structure of AR, ARMA and state space systems, Yule-Walker equations, factorization of rational spectral densities and Kalman filtering. Finally, there is a discussion of Granger causality, linear dynamic factor models and (G)ARCH models. The book provides a solid basis for advanced mathematics students and researchers in fields such as data-driven modeling, forecasting and filtering, which are important in statistics, control engineering, financial mathematics, econometrics and signal processing, among other subjects.. | | 出版日期 | Textbook 2022 | | 關(guān)鍵詞 | Time Series Analysis; Multivariate Time Series; Weakly Stationary Processes; Linear Dynamical Systems; T | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-031-13213-1 | | isbn_softcover | 978-3-031-13212-4 | | isbn_ebook | 978-3-031-13213-1Series ISSN 0930-0325 Series E-ISSN 2197-7186 | | issn_series | 0930-0325 | | copyright | Springer Nature Switzerland AG 2022 |
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