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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. FeaturesComprehensive coverage of classical time series models including ARIMA, state-space models, and spectral methodsTwo new chapters on predictive and generative AI, introducing cutting-edge methods like transformers, variational autoencoders, and diffusion modelsPractical examples and illustrations using R, demonstrating the application of both classical and AI-based approaches to real-world time series dataEmphasis on the integration of classical statistical rigor with the flexibility and scalability of AI methodsClear explanations and intuitive insights, making advanced concepts accessible to a broad audienceUpdated content reflecting the latest developments in time series analysis, with a focus on modern, high-dimensional, and nonlinear data challengesThe 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.
Deborah Mikyo Oh
Serena Quarta
Audrey R. Giles
Roehl Sybing
David Doane, Lori Seward
Douglas Lind, Samuel Wathen, William Marchal
Peter A. Rogerson
Norean Sharpe, Paul Velleman, Richard De Veaux
Brody Heritage, Kellie Bennett, Peter Allen
Craig K. Enders
Bonita M. Kolb
Paul Wilkins, Pete Sanders
Gouri K. Bhattacharyya, Richard A. Johnson
Alan Zimmerman, Jim Blythe, Nektarios Tzempelikos
Philip O'Regan
Felix C. ) Tropf, Melinda C. Mills, Nicola Barban
Bernard Grofman
Ivory A. Toldson
Graham R. Gibbs
David Coghlan
Matthew Loveless
D. V. Lindley, W. F. Scott
Sarah Easter Collins
Frida Skybäck
Liz Moore
Colleen Hoover
Kristin Fägerskjöld
Armando Lucas Correa
Sharon Bolton
Ruth Lillegraven
Santa Montefiore
Kelly Rimmer
Martha Hall Kelly
Svenja O'Donnell
Nathan Harris
Holly Jackson
Louise Boije af Gennäs
Tina Norin
Kristin Fägerskjöld
Pernilla Lindroos
Anna Lönnqvist
Jessie Burton
Malin Nordström
Marc Levy
Emma Donoghue
Snezana Lindskog, Andie Lindskog
Tony Samuelsson
Andrev Walden
Tina Harnesk
Kathrine Nedrejord
Anthony Doerr
Marieke Lucas Rijneveld
Lauren Groff
Claire Messud
Claire Lynch
Sebastian Barry
Ray Celestin
Simon Stranger
Alice Munro
Linda Boström Knausgård
Marit Kapla
Ulf Stark
Sarah Easter Collins
Liz Moore
Ursula Scavenius
Colleen Hoover
Michel Jean
