Discrete Time Series

The analysis and modeling of time series has been an active research area for more than 100 years, with the main focus on time series having a continuous range consisting of real numbers or real vectors. It took until the 1980s for the first papers on discrete-valued time series to appear. In the 2000s, a rapid increase in research activity was noted, but only in the last few years was a certain maturity and consolidation of the area of discrete-valued time series observed. This reprint is a collection of articles on a wide range of topics on discrete-valued time series (especially count time series), covering stochastic models and methods for their analysis, univariate and multivariate time series, applications of time series methods to risk analysis, statistical process control, and many more. The proposed approaches and concepts are thoroughly discussed and illustrated with several real-world data examples.

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"As an introduction to techniques for analyzing discrete time series, this textbook explains probability models, the spectral density function, time-invariant linear systems, state-space models, nonlinear models, and multivariate time series models."-"Book News, Inc."
This tidy book is a highly readable, introductory survey to the topic of modern time series analysis. It excels in its ability to focus on the more intuitive aspects of analysis and model identification. The discussion of both time- and frequency-domain approaches is reasonably balanced, and Kalman filtering is also introduced.