Boris P. Bezruchko, Dmitry A. Smirnov

Extracting Knowledge From Time Series (eBook, PDF)

An Introduction to Nonlinear Empirical Modeling

• Format: PDF

Produktbeschreibung

This book addresses the fundamental question of how to construct mathematical models for the evolution of dynamical systems from experimentally-obtained time series. It places emphasis on chaotic signals and nonlinear modeling and discusses different approaches to the forecast of future system evolution. In particular, it teaches readers how to construct difference and differential model equations depending on the amount of a priori information that is available on the system in addition to the experimental data sets. This book will benefit graduate students and researchers from all natural sciences who seek a self-contained and thorough introduction to this subject.

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Produktdetails

• Verlag: Springer Berlin Heidelberg
• Seitenzahl: 410
• Erscheinungstermin: 3. September 2010
• Englisch
• ISBN-13: 9783642126017
• Artikelnr.: 37373524

Inhaltsangabe

Models And Forecast.- The Concept of Model. What is Remarkable in Mathematical Models.- Two Approaches to Modelling and Forecast.- Dynamical (Deterministic) Models of Evolution.- Stochastic Models of Evolution.- Modeling From Time Series.- Problem Posing in Modelling from Data Series.- Data Series as a Source for Modelling.- Restoration of Explicit Temporal Dependencies.- Model Equations: Parameter Estimation.- Model Equations: Restoration of Equivalent Characteristics.- Model Equations: "Black Box" Reconstruction.- Practical Applications of Empirical Modelling.- Identification of Directional Couplings.- Outdoor Examples.

Rezensionen

From the reviews: "Extracting knowledge from time series is a very neat title-it exactly encapsulates the topic which the authors hope to cover in this volume. ... This is admirable, and the result is valuable. ... This is overall a useful volume for providing an overview of the area ... ." (Michael Small, Mathematical Reviews, Issue 2012 d)