2002 | OriginalPaper | Buchkapitel
Determining Minimum Embedding Dimension from Scalar Time Series
verfasst von : Liangyue Cao
Erschienen in: Modelling and Forecasting Financial Data
Verlag: Springer US
Enthalten in: Professional Book Archive
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
Determining embedding dimension is considered as one of the most important steps in nonlinear time series modelling and prediction. A number of methods have been developed in determining the minimum embedding dimension since the early study of nonlinear time series analysis. Some of the methods are briefly reviewed in this chapter. The false nearest neighbor and the averaged false nearest neighbor methods are described in details, given the methods have been widely used in the literature. Several real economic time series are tested to demonstrate applications of the methods.