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Chaos and Forecasting (Nonlinear Time Series and Chaos, 2),Used
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It is now generally recognised that very simple dynamical systems can produce apparently random behaviour. In the last couple of years, attention has turned to focus on the flip side of this coin: randomlooking time series (or randomlooking 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 lowdimensional attractor). In either case, a longterm prediction will be possible only in probabilistic terms. However, in the very short term, random systems will still be unpredictable but lowdimensional chaotic ones may be predictable (appearances to the contrary). The Royal Society held a twoday discussion meeting on topics covering diverse fields, including biology, economics, geophysics, meteorology, statistics, epidemiology, earthquake science and many others. Each topic was 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 artificiallygenerated time series and real data from context in the abovementioned diverse fields. This book marks a rather special and rare occasion on which prominent scientists from different areas converge on the same theme. It forms an informative introduction to the science of chaos, with special reference to real data.
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