Abstract
The analysis of experimental time series for prediction based on a dynamical systems approach remains a challenging problem. A scalar time series x(t) ∈ ℜ can be considered to be a single component of a many-dimensional process of unknown dimension. In this chapter we adopt the working hypothesis that many classes of experimental time series may be analysed within the framework of a dynamical systems approach. We are assuming that the state of a system is given by a point 5 evolving in a multidimensional state space Γ. Then the motion of s in Γ characterises the dynamics of the system.
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© 2002 Springer-Verlag London
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Hazarika, N. (2002). Dynamical Systems Perspective and Embedding. In: Shadbolt, J., Taylor, J.G. (eds) Neural Networks and the Financial Markets. Perspectives in Neural Computing. Springer, London. https://doi.org/10.1007/978-1-4471-0151-2_13
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DOI: https://doi.org/10.1007/978-1-4471-0151-2_13
Publisher Name: Springer, London
Print ISBN: 978-1-85233-531-1
Online ISBN: 978-1-4471-0151-2
eBook Packages: Springer Book Archive