embedding reconstruction methodology for short time series – application to large el niño events

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ID: 159958
2010
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Abstract
We propose an alternative approach for the embedding space reconstruction method for short time series. An <i>m</i>-dimensional embedding space is reconstructed with a set of time delays including the relevant time scales characterizing the dynamical properties of the system. By using a maximal predictability criterion a <i>d</i>-dimensional subspace is selected with its associated set of time delays, in which a local nonlinear blind forecasting prediction performs the best reconstruction of a particular event of a time series. An locally unfolded <i>d</i>-dimensional embedding space is then obtained. The efficiency of the methodology, which is mathematically consistent with the fundamental definitions of the local nonlinear long time-scale predictability, was tested with a chaotic time series of the Lorenz system. When applied to the Southern Oscillation Index (SOI) (observational data associated with the El Niño-Southern Oscillation phenomena (ENSO)) an optimal set of embedding parameters exists, that allows constructing the main characteristics of the El Niño 1982–1983 and 1997–1998 events, directly from measurements up to 3 to 4 years in advance.
Reference Key
astudillo2010nonlinearembedding Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;H. F. Astudillo;F. A. Borotto;R. Abarca-del-Rio
Journal BMC research notes
Year 2010
DOI
10.5194/npg-17-753-2010
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