Predictability of complex dynamical systems
Author(s)
Bibliographic Information
Predictability of complex dynamical systems
（Springer series in synergetics, 69）
Springer, c1996
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Library, Research Institute for Mathematical Sciences, Kyoto University数研
C||Predicta||bility-196057125
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Note
Includes bibliographical references and index
Description and Table of Contents
Description
This book was originally conceived as a continuation in theme of the collec- tive monograph Limits of Predictability (Yu. A. Kravtsov, Ed. , Springer Series in Synergetics, Vol. 60, Springer-Verlag, Heidelberg, 1993). The main thrust of that book was to examine the various effects and factors (system non- stationarity, measurement noise, predictive model accuracy, and so on) that may limit, in a fundamental fashion, our ability to mathematically predict physical and man-made phenomena and events. Particularly interesting was the diversity of fields from which the papers and examples were drawn, in- cluding climatology, physics, biophysics, cybernetics, synergetics, sociology, and ethnogenesis. Twelve prominant Russian scientists, and one American (Prof. A. J. Lichtman) discussed their philosophical and scientific standpoints on the problem of the limits of predictability in their various fields. During the preparation of that book, the editor (Yu. A. K) had the great pleasure of interacting with world-renowned Russian scientists such as oceanologist A. S. Monin, geophysicist V. I. Keilis-Borok, sociologist I. V. Bestuzhev-Lada, histo- rian L. N. Gumilev, to name a few. Dr.
Angela M. Lahee, managing editor of the Synergetics Series at Springer, was enormously helpful in the publishing of that book. In 1992, Prof. H. Haken along with Dr. Lahee kindly supported the idea of publishing a second volume on the theme of nonlinear system predictability, this time with a more international flavor.
Table of Contents
1 Introduction.- 2 Time Series Analysis: The Search for Determinism.- Method to Discriminate Against Determinism in Time Series Data.- Observing and Predicting Chaotic Signals: Is 2% Noise Too Much?.- A Discriminant Procedure for the Solution of Inverse Problems for Non-stationary Systems.- Classifying Complex, Deterministic Signals.- 3 Dynamical Modeling and Forecasting Algorithms.- Strategy and Algorithms of Dynamical Forecasting.- Parsimony in Dynamical Modeling.- The Bifurcation Paradox: The Final State Is Predictable If the Transition Is Fast Enough.- 4 Prediction of Biological Systems.- Models and Predictability of Biological Systems.- Limits of Predictability for Biospheric Processes.- 5 Analysis and Forecasting of Financial Data.- The Application of Wave Form Dictionaries to Stock Market Index Data.- 6 Socio-Political and Global Problems.- Messy Futures and Global Brains.
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