Probabilistic time series modelling
Time series are studied in a variety of disciplines and appear in many modern applications such as financial time series prediction, video-tracking, music analysis, control and genetic sequence analysis. This widespread interest at times obscures the commonalities in the developed models and techniques. A central aim of this book is to attempt to make modern time series techniques, specifically those based on probabilistic modelling, accessible to a broad range of researchers.
In order to achieve this goal, leading researchers that span the more traditional disciplines of statistics, control theory, engineering and signal processing, and the more recent areas of machine learning and pattern recognition, have been brought together to discuss advancements and developments in their respective fields. In addition, the book makes extensive use of the graphical models framework. This framework facilitates the representation of many classical models and provides insight into the computational complexity of their implementation. Furthermore, it enables to easily envisage new models tailored for a particular environment. For example, the book discusses novel state space models and their application in signal processing including condition monitoring and tracking. The book also describes modern developments in the machine learning community applied to more traditional areas of control theory.
The effective application of probabilistic models in the real world is gaining pace, largely through increased computational power which brings more general models into consideration through carefully developed implementations.