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Proceedings Paper

Best judge of closeness of delay vectors in embedding space? The Euclidean metric...not!
Author(s): Daniel B. Murray
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Paper Abstract

An improved method for forecasting a time series is demonstrated. Forecasts are based on future behavior of nearest neighbors in an embedding space of time delay vectors. The metric of this space is generalized, rather than Euclidean. Reduction of forecast error and high probability of improved forecast are demonstrated for various chaotic time series. The approach also works for chaotic time series with added measurement noise. In addition, a lower bound on forecast error is calculated.

Paper Details

Date Published: 18 November 1993
PDF: 15 pages
Proc. SPIE 2038, Chaos in Communications, (18 November 1993); doi: 10.1117/12.162685
Show Author Affiliations
Daniel B. Murray, Okanagan Univ. College (Canada)

Published in SPIE Proceedings Vol. 2038:
Chaos in Communications
Louis M. Pecora, Editor(s)

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