The Analysis of Time Series: An Introduction, Sixth Edition (Google eBook)

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CRC Press, Nov 19, 2013 - Mathematics - 352 pages
2 Reviews

Since 1975, The Analysis of Time Series: An Introduction has introduced legions of statistics students and researchers to the theory and practice of time series analysis. With each successive edition, bestselling author Chris Chatfield has honed and refined his presentation, updated the material to reflect advances in the field, and presented interesting new data sets.

The sixth edition is no exception. It provides an accessible, comprehensive introduction to the theory and practice of time series analysis. The treatment covers a wide range of topics, including ARIMA probability models, forecasting methods, spectral analysis, linear systems, state-space models, and the Kalman filter. It also addresses nonlinear, multivariate, and long-memory models. The author has carefully updated each chapter, added new discussions, incorporated new datasets, and made those datasets available for download from A free online appendix on time series analysis using R can be accessed at

Highlights of the Sixth Edition:

  • A new section on handling real data
  • New discussion on prediction intervals
  • A completely revised and restructured chapter on more advanced topics, with new material on the aggregation of time series, analyzing time series in finance, and discrete-valued time series
  • A new chapter of examples and practical advice
  • Thorough updates and revisions throughout the text that reflect recent developments and dramatic changes in computing practices over the last few years

The analysis of time series can be a difficult topic, but as this book has demonstrated for two-and-a-half decades, it does not have to be daunting. The accessibility, polished presentation, and broad coverage of The Analysis of Time Series make it simply the best introduction to the subject available.


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Chapter 1 Introduction
Chapter 2 Simple Descriptive Techniques
Chapter 3 Some TimeSeries Models
Chapter 4 Fitting TimeSeries Models in the Time Domain
Chapter 5 Forecasting
Chapter 6 Stationary Processes in the Frequency Domain
Chapter 7 Spectral Analysis
Chapter 8 Bivariate processes
Chapter 12 Multivariate TimeSeries Modelling
Chapter 13 Some More Advanced Topics
Chapter 14 Examples and Practical Advice
Fourier Laplace and zTransforms
Dirac Delta Function
Covariance and Correlation
Some MINITAB and SPLUS Commands
Answers to Exercises

Chapter 9 Linear Systems
Chapter 10 StateSpace Models and the Kalman Filter
Chapter 11 NonLinear Models

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