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    Classification of process dynamics with Monte Carlo singular spectrum analysis

    Access Status
    Fulltext not available
    Authors
    Jemwa, G.
    Aldrich, Chris
    Date
    2006
    Type
    Journal Article
    
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    Citation
    Jemwa, G. and Aldrich, C. 2006. Classification of process dynamics with Monte Carlo singular spectrum analysis. Computers and Chemical Engineering. 30: pp. 816-831.
    Source Title
    Computers and Chemical Engineering
    ISSN
    00981354
    URI
    http://hdl.handle.net/20.500.11937/13300
    Collection
    • Curtin Research Publications
    Abstract

    Metallurgical and other chemical process systems are often too complex to model from first principles. In such situations the alternative is to identify the systems from historic process data. Such identification can pose problems of its own and before attempting to identify the system, it may be important to determine whether a particular model structure is justified by the data before building the model. For example, the analyst may wish to distinguish between nonlinear (deterministic) processes and linear (stochastic) processes to justify the use of a particular methodology for dealing with the time series observations, or else it may be important to distinguish between different stochastic models. In this paper the use of a linear method called singular spectrum analysis (SSA) to classify time series data is discussed. The method is based on principal component analysis of an augmented data set consisting of the original time series data and lagged copies of the data. In addition, a nonlinear extension of SSA based on kernel-based eigenvalue decomposition is introduced. The usefulness of kernel SSA as a complementary tool in the search for evidence of nonlinearity in time series data or for testing other hypotheses about such data is illustrated by simulated and real-world case studies.

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