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Locally optimized prediction of nonlinear systems: stochastic and deterministic

By Leonard A. Smith

Abstract

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

Topics: HA Statistics
Publisher: World Scientific Publishing
Year: 1995
OAI identifier: oai:eprints.lse.ac.uk:32775
Provided by: LSE Research Online
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