The Analysis of Time Series

An Introduction with R

Haipeng Xing, Chris Chatfield

• Publisher: Chapman and Hall/CRC
• Year: 2026
• Print ISBN:
  9781041026334
  9781041085867
• eBook ISBN:
  9781040615263
• Edition: 8
• Topic: Mathematik
• Subtopic: Wahrscheinlichkeitsrechnung & Statistiken

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.

Table of contents

• Cover Page
• Half-Title Page
• Series Page
• Title Page
• Copyright Page
• Dedication Page
• Epigraph Page
• Contents
• Preface to the Eighth Edition
• Abbreviations and Notations
• 1 Introduction
• 2 Basic Descriptive Techniques
• 3 Some Linear Time Series Models
• 4 Fitting Time Series Models in the Time Domain
• 5 Forecasting
• 6 Stationary Processes in the Frequency Domain
• 7 Spectral Analysis
• 8 Bivariate Processes
• 9 Linear Systems
• 10 State-Space Models and the Kalman Filter
• 11 Non-Linear Models
• 12 Volatility Models
• 13 Multivariate Time Series Modelling
• 14 Predictive AI for Time Series
• 15 Generative AI for Time Series
• Appendix A Fourier, Laplace, and z-Transforms
• Appendix B Dirac Delta Function
• Appendix C Covariance and Correlation
• Answers to Exercises
• Bibliography
• Index