Abstract
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.
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Cao, L. (2002). Determining Minimum Embedding Dimension from Scalar Time Series. In: Soofi, A.S., Cao, L. (eds) Modelling and Forecasting Financial Data. Studies in Computational Finance, vol 2. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-0931-8_3
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DOI: https://doi.org/10.1007/978-1-4615-0931-8_3
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