Follow Here To Purchase Time Series Analysis and Forecasting by Example (Wiley Series in Probability and Statistics)

Author: Soren Bisgaard & Murat Kulachi

Publisher: Wiley-Blackwell
ISBN: 978-1-118-05694-3


Time Series Analysis & Forecasting by Example is an introduction to time series analysis for students who have some background in statistics. The book starts with a focus on serial correlation which is one of the hurdles for students who transition to time series analysis from basic statistics.

Introductory topics covered deal with basic concepts such as impulse response function, Wold decomposition, etc. There is a good emphasis on graphical tools and characteristics of generating good plots which are useful for practical analysis.  The section is rounded off by a discussion on bad graphics.

The rest of the book is very well structured - starting with stationary models and discussing details of the ARMA and checking for stationarity. The part on non-stationary models deals clearly with standard issues of detection, ARIMA.

Section on time series model selection covers issues including the use of the AIC, bias corrected information criteria and Bayesian information criteria. The section also discusses comparing impulse response models for competing models.

Students should find the example analysis cases for a chemical process and seasonal company sales model very helpful in the practical aspects of building time series models. The practical computing example for impulse response function with a spreadsheet helps to build confidence in model building.

Sufficient depth is provided into issues with ARIMA model including constant terms, over differencing and under differencing, and handling missing values in time series models, covering most issues which are of concern to basic to intermediate level analysis.

Transfer function model discussion includes identifying the transfer function, modeling the noise, general methodology and forecasting. Additional topics such as spurious relationships, process regime changes, and multiple time series are included towards the end of the book making it a comprehensive guide.

The coverage of all the basic topics with real world examples should appeal to a wide range of fields where time series methods are used. While the book adopts a clear and simplified approach the depth of issues is not compromised while keeping an eye on implementation.

Time Series Analysis and Forecasting by Example is well recommended as a great introductory book for students transitioning from general statistics to time series as well as a good source book for intermediate level time series model builders.


Follow Here To Purchase Time Series Analysis and Forecasting by Example (Wiley Series in Probability and Statistics)