Abstract
In the analysis of most experimental time series, the question of how to best represent the data has always had an obvious answer - as a time series. However, with the discovery of chaotic attractors, we have learned that the data from many experimental systems can be better represented in a reconstructed state space [1]. This reveals features of the dynamics that were not apparent in the original time series. These state space reconstructions may simply be considered a generalization of the phase space plots used in classical mechanics.
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© 1992 Springer Science+Business Media New York
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Breeden, J.L., Packard, N.H. (1992). Learning Optimal Representations. In: Bountis, T. (eds) Chaotic Dynamics. NATO ASI Series, vol 298. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-3464-8_3
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DOI: https://doi.org/10.1007/978-1-4615-3464-8_3
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