Analysis of Time Series

author: Riccardo Bellazzi, University of Pavia
published: Feb. 25, 2007,   recorded: June 2005,   views: 26849

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The study of time series is an essential aspect of Intelligent Data Analysis. The field is very broad, and it has been treated with very different methodological approaches, ranging from differential equations to stochastic models and to AI-based systems. The lesson will present time series analysis as a part of the general problem of modelling dynamical systems. The framework of systems’ theory will provide a general view of such problem, and it will permit to coherently overview the majority of the time series analysis approaches. In more detail, the principles of systems theory will be first discussed; the concept of “dynamical system” will be investigated and some results of systems theory will be presented. The notions of state, equilibrium, linearity, observability and reachability will be discussed. Some modelling tools will be then introduced, ranging from black-box to structural models. Stochastic linear and non linear models will be briefly described, including AR, MA, and ARMAX models. Moreover, a method to obtain structural information from input/output data will be introduced. The lesson will finally show how the knowledge on systems dynamics can be effectively exploited in the time series clustering problem. Distance-based, model-based and template-based will be revisited in order to account for information on the systems dynamics.

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Reviews and comments:

Comment1 Suleiman, January 27, 2008 at 11:30 p.m.:

Really Good Professor, and a good lecture - Even though that the last 30 minutes were for time series - and first one hour for dynamical systems.

Comment2 Bitcoin Homework, October 1, 2018 at 1:05 p.m.:

"The lesson will finally show how the knowledge on systems dynamics can be effectively exploited in the time series clustering problem."
- Has it shown anything?

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