Introductory Time Series Analysis Textbook

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SUMMARY

The discussion centers on recommendations for a comprehensive textbook on time series analysis, particularly for a course covering topics such as trend analysis, ARMA and ARIMA models, and the Kalman filter. Participants suggest Wei's textbook for its unique insights on outliers and its practical examples using Autobox software. Additionally, Dan Simon's book is highlighted for its accessibility regarding the Kalman filter. The conversation emphasizes the need for a resource that serves both as a learning tool and a reference guide.

PREREQUISITES
  • Understanding of time series analysis concepts
  • Familiarity with ARMA and ARIMA models
  • Knowledge of the Kalman filter
  • Experience with statistical software, specifically Autobox
NEXT STEPS
  • Research Wei's textbook on time series analysis for its insights on outliers
  • Explore Dan Simon's book for a beginner-friendly approach to the Kalman filter
  • Investigate the use of Autobox software for practical applications in time series
  • Study advanced topics in time series, including transfer function models and interventions
USEFUL FOR

Students and professionals in statistics, data analysis, and econometrics who are seeking a solid foundation in time series analysis and practical applications using software tools like Autobox.

hsu
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I'll be taking an introductory course on time series analysis in the spring, and we will be using the instructor's online notes as the "textbook". My previous experiences with such instructor's notes have been that they contain only the essentials of the course and aren't really useful as references. Besides, I always like using a physical textbook.

So, can anyone recommend a good textbook for this subject that is both clear for learning from and useful as a reference? The list of topics to be covered is:

Trend analysis, trend-based methods (moving averages, exponential smoothing), stationary processes, ARMA and ARIMA models, spectrum and its estimation, frequency filtration, seasonal models, multivariate models, Kalman filter.

By the way, another professor who sometimes teaches this course at my university has used this textbook:

https://www.amazon.com/dp/0321322169/?tag=pfamazon01-20

in the past; would that be one to consider?

Thanks!
 
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I liked Shumway and Stoffer for time series, but for the Kalman filter Dan Simon's book is exceptionally friendly.
 
Hsu,

The Wei textbook is excellent for a number of reasons. It describes what to look for that other textbooks don't address. Chapter 9 discussed outliers and how important they are in order to model the data (ie additive outliers(AO), innovative outliers(IO)). Example 9.5 uses our software (Autobox) in the example. It doesn't discuss level shifts (ie 0,0,0,0,1,1,1,1,etc) or seasonal pulses (ie 0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,etc for monthly data).

The transfer function models examples also used Autobox, but do not show the use of them with interventions as the book was written back in 1990 and did not include those advances over time (but what other textbook does? :) You can use Autobox as a student with ~700 time series from textbooks and of course Wei's.

See the ackowledgements section in the front for our name.

We would be happy to answer any questions here or off-line.

www.autobox.com

Regards
Tom Reilly
 
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