Embedding reconstruction methodology for short time series – application to large El Niño events

• Published in: Nonlinear processes in geophysics Vol. 17; no. 6; pp. 753 - 764
• Main Authors: Astudillo, H F, Borotto, F A, Abarca-del-Rio, R
• Format: Journal Article
• Language: English
• Published: Gottingen Copernicus GmbH 01.01.2010; European Geosciences Union (EGU); Copernicus Publications
• Subjects: Hurricanes; Ocean, Atmosphere; Research methodology; Sciences of the Universe; Time series; PREDICTABILITY; ENSO; MONSOON; STATE; SYSTEMS; PREDICTION MODEL; NOISE; LYAPUNOV EXPONENT
• ISSN: 1607-7946; 1023-5809; 1607-7946
• DOI: 10.5194/npg-17-753-2010

We propose an alternative approach for the embedding space reconstruction method for short time series. An m-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 d-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 d-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.