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Locally optimized prediction of nonlinear systems: stochastic and deterministic

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It is now generally recognized that very simple dynamical systems can produce apparently random behaviour. Attention has recently turned to focus on the flip-side of this coin: random-looking time series (or random-looking patterns in space) may indeed be the result of very complicated processes or "real noise", but they may equally well be produced by some very simple mechanism (a low-dimensional attractor). In either case, a long-term prediction will be possible only in probabilistic terms. However, in the very short term, random systems will still be unpredictable but low-dimensional chaotic ones may be predictable (appearances to the contrary). The Royal Society held a two-day discussion meeting on topics covering diverse fields, including biology, economics, geophysics, meterology, statistics, epidemiology, earthquake science and many others, each topic covered by a leading expert in the field. The meeting dealt with different basic approaches to the problem of chaos and forecasting, and covered applications to nonlinear forecasting of both artificially-generated time series and real data from context in the above-mentioned diverse fields. This book forms an introduction to the science of chaos, with special reference to real data.

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en

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http://eprints.lse.ac.uk/32775/

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