Change Point Analysis for Time Series
This volume provides a comprehensive survey that covers various modern methods used for detecting and estimating change points in time series and their models. The book primarily focuses on asymptotic theory and practical applications of change point analysis. The methods discussed in the book go be...
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| Main Authors: | , |
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| Corporate Author: | |
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Cham :
Springer Nature Switzerland : Imprint: Springer,
2024.
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| Edition: | 1st ed. 2024. |
| Series: | Springer Series in Statistics,
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| Subjects: | |
| Online Access: | https://doi.org/10.1007/978-3-031-51609-2 |
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| Summary: | This volume provides a comprehensive survey that covers various modern methods used for detecting and estimating change points in time series and their models. The book primarily focuses on asymptotic theory and practical applications of change point analysis. The methods discussed in the book go beyond the traditional change point methods for univariate and multivariate series. It also explores techniques for handling heteroscedastic series, high-dimensional series, and functional data. While the primary emphasis is on retrospective change point analysis, the book also presents sequential "on-line" methods for detecting change points in real-time scenarios. Each chapter in the book includes multiple data examples that illustrate the practical application of the developed results. These examples cover diverse fields such as economics, finance, environmental studies, and health data analysis. To reinforce the understanding of the material, each chapter concludes with several exercises. Additionally, the book provides a discussion of background literature, allowing readers to explore further resources for in-depth knowledge on specific topics. Overall, "Change Point Analysis for Time Series" offers a broad and informative overview of modern methods in change point analysis, making it a valuable resource for researchers, practitioners, and students interested in analyzing and modeling time series data. |
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| Physical Description: | 1 online resource (XIII, 545 p. 36 illus.). 30 illus. in color. |
| ISBN: | 9783031516092 |
| ISSN: | 2197-568X |
| DOI: | 10.1007/978-3-031-51609-2 |