The Analysis of Time Series: An Introduction, Sixth Edition

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CRC Press, Mar 30, 2016 - Mathematics - 352 pages
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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 www.crcpress.com. A free online appendix on time series analysis using R can be accessed at http://people.bath.ac.uk/mascc/TSA.usingR.doc.

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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Contents

2 Simple Descriptive Techniques
11
3 Some TimeSeries Models
33
4 Fitting TimeSeries Models in the Time Domain
55
5 Forecasting
73
6 Stationary Processes in the Frequency Domain
107
7 Spectral Analysis
121
8 Bivariate processes
155
9 Linear Systems
169
13 Some More Advanced Topics
255
14 Examples and Practical Advice
277
A Fourier Laplace and zTransfonns
295
B Dirac Delta Function
299
C Covariance and Correlation
301
D Some MINITAB and SPLUS Commands
303
Answers to Exercises
307
References
315

10 StateSpace Models and the Kalman Filter
203
11 NonLinear Models
217
12 Multivariate TimeSeries Modelling
241
Back Cover
334
Copyright

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About the author (2016)

Chatfield, University of Bath, United Kingdom.

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