Time Series Analysis

A modern and accessible guide to the analysis of introductory time series data

Featuring an organized and self-contained guide, Time Series Analysis provides a broad introduction to the most fundamental methodologies and techniques of time series analysis. The book focuses on the treatment of univariate time series by illustrating a number of well-known models such as ARMA and ARIMA.

Providing contemporary coverage, the book features several useful and newlydeveloped techniques such as weak and strong dependence, Bayesian methods, non-Gaussian data, local stationarity, missing values and outliers, and threshold models. Time Series Analysis includes practical applications of time series methods throughout, as well as:

Real-world examples and exercise sets that allow readers to practice the presented methods and techniques
Numerous detailed analyses of computational aspects related to the implementation of methodologies including algorithm efficiency, arithmetic complexity, and process time
End-of-chapter proposed problems and bibliographical notes to deepen readers’ knowledge of the presented material
Appendices that contain details on fundamental concepts and select solutions of the problems implemented throughout
A companion website with additional data fi les and computer codes
Time Series Analysis is an excellent textbook for undergraduate and beginning graduate-level courses in time series as well as a supplement for students in advanced statistics, mathematics, economics, finance, engineering, and physics. The book is also a useful reference for researchers and practitioners in time series analysis, econometrics, and finance.