Abstract
In this paper we propose an alternative definition to the embedding dimension that we call predictive dimension. This dimension does not refer to the number of delayed variables needed to characterize the system but to the best predictions that can be obtained for the system. This kind of definition is particularly useful in a forecasting context because it leads to the same value of the traditional embedding dimension for chaotic time series and it is always finite for stochastic ones.
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Guègan, D., Lisi, F. (2000). Predictive dimension: an alternative definition to embedding dimension. In: Bethlehem, J.G., van der Heijden, P.G.M. (eds) COMPSTAT. Physica, Heidelberg. https://doi.org/10.1007/978-3-642-57678-2_40
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DOI: https://doi.org/10.1007/978-3-642-57678-2_40
Publisher Name: Physica, Heidelberg
Print ISBN: 978-3-7908-1326-5
Online ISBN: 978-3-642-57678-2
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