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Author Small, Michael
Source CiteSeerX
Content type Text
File Format PDF
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Global Model ♦ Time Series Data ♦ Slow Dynamic ♦ Di Erent Time Scale ♦ New Method ♦ Inter-cycle Uctuations ♦ Model Out-performs ♦ Statistical De-trending ♦ Nonlinear Dynamical System ♦ Fast Dynamic ♦ Insu Cient ♦ Deterministic Radial Basis Function Network ♦ Single Model ♦ Fast Intra-cycle Variation ♦ Di Cult ♦ Standard Method ♦ Dynamic Scalar Time Series ♦ Sound Wave ♦ Individual System ♦ Many Time Series Exhibit Dynamic ♦ Deterministic Dynamic ♦ Standard Way
Abstract Abstract. Many time series exhibit dynamics over vastly di®erent time scales. The standard way to capture this behavior is to assume that the slow dynamics are a \trend", to de-trend the data, and then to model the fast dynamics. However, for nonlinear dynamical systems this is gen-erally insu±cient. In this paper we describe a new method, utilizing two distinct nonlinear modeling architectures to capture both fast and slow dynamics. Slow dynamics are modeled with the method of analogues, and fast dynamics with a deterministic radial basis function network. When combined the resulting model out-performs either individual system. 1 Fast and slow dynamics Scalar time series often exhibit deterministic dynamics on very di®erent time scales (see Fig. 1). For example, sound waves exhibit fast intra-cycle variation and slow inter-cycle °uctuations. It is di±cult for a single model to describe both behaviors simultaneously. A standard method for treating such data is to ¯rst apply some statistical de-trending and to then model the residuals. In ¯nancial
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study
Learning Resource Type Article