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The Analysis of Time Series: An Introduction with R
Hardcover
Series: Chapman & Hall/CRC Texts in Statistical Science
ISBN13: 9781041085867
Publisher: CRC Press
Published: Aug 16 2026
Pages: 402
Weight: 1.68
Height: 0.94 Width: 6.14 Depth: 9.21
Language: English
The field of time series analysis has undergone a remarkable transformation since the publication of the seventh edition of this book. While classical statistical models such as autoregressive integrated moving average (ARIMA), state-space models, and spectral methods remain essential, the rise of artificial intelligence (AI) has introduced groundbreaking approaches to modelling, forecasting, and generating time-dependent data. This eighth edition of The Analysis of Time Series: An Introduction with R reflects these advancements with the addition of two new chapters: Predictive AI for Time Series and Generative AI for Time Series. These chapters bridge the gap between traditional time series methods and cutting-edge AI techniques, offering readers a comprehensive and integrated perspective on the field.
Features
- Comprehensive coverage of classical time series models including ARIMA, state-space models, and spectral methods
- Two new chapters on predictive and generative AI, introducing cutting-edge methods like transformers, variational autoencoders, and diffusion models
- Practical examples and illustrations using R, demonstrating the application of both classical and AI-based approaches to real-world time series data
- Emphasis on the integration of classical statistical rigor with the flexibility and scalability of AI methods
- Clear explanations and intuitive insights, making advanced concepts accessible to a broad audience
- Updated content reflecting the latest developments in time series analysis, with a focus on modern, high-dimensional, and nonlinear data challenges
The Analysis of Time Series: An Introduction with R, Eighth Edition is designed for students, researchers, and practitioners in statistics, as well as in finance, economics, climate science, health, and engineering. It serves as both a foundational text for those new to time series analysis and a valuable resource for experienced analysts seeking to engage with the rapidly evolving landscape of predictive and generative AI. With its balance of theory, practical implementation, and real-world examples, the book is ideal for use in academic courses, professional training, and self-study.
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