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New tools in non-linear modelling and prediction

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  • Antonia Jones
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    Abstract

    In this paper we give an account of a new change of perspective in non-linear modelling and prediction as applied to smooth systems. The core element of these developments is the Gamma test a non-linear modelling and analysis tool which allows us to examine the nature of a hypothetical input/output relationship in a numerical data-set. In essence, the Gamma test allows us to efficiently calculate that part of the variance of the output which cannot be accounted for by the existence of any smooth model based on the inputs, even though this model be unknown. A key aspect of this tool is its speed: the Gamma test has time complexity O( $M \log M$ ), where M is the number of data-points. For data-sets consisting of a few thousand points and a reasonable number of attributes, a single run of the Gamma test typically takes a few seconds. Around this essentially simple procedure a new set of analytical tools has evolved which allow us to model smooth non-linear systems directly from the data with a precision and confidence that hitherto was inaccessible. In this paper we briefly describe the Gamma test, its benefits in model identification and model building, and then in more detail explain and motivate the procedures which facilitate a Gamma analysis. We briefly report on a case study applying these ideas to the practical problem of predicting level and flow rates in the Thames valley river basin. Finally we speculate on the future development and enhancement of these techniques into areas such as datamining and the production of complex non-linear models directly from data via graphical representations of process charts and automated Gamma analysis of each input-output node. Copyright Springer-Verlag Berlin/Heidelberg 2004

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    Bibliographic Info

    Article provided by Springer in its journal Computational Management Science.

    Volume (Year): 1 (2004)
    Issue (Month): 2 (07)
    Pages: 109-149

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    Handle: RePEc:spr:comgts:v:1:y:2004:i:2:p:109-149

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    Related research

    Keywords: Gamma test; noise estimation; significant variable identification;

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