Timeseries appear in a variety of disciples, from finance to physics, computer science to biology. The origins of the subject and diverse applications in the engineering and physics literature at times obscure the commonalities in the underlying models and techniques. Modern timeseries applications include financial timeseries prediction, video-tracking, music analysis, control theory and genetic sequence analysis.
Because of the wide base of application areas, having a common description of the models is useful in transferring ideas between the various communities. Graphical models, a marriage between graph and probability theory provide a compact way to represent models and transfer ideas. In particular many classical models can be readily understood in terms of graphical models, which also provides insight into the computational complexity of their implementation. Using graphical models it is easy to envisage new models tailored for a particular environment. 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.