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Time series prediction : forecasting the future and understanding the past : proceedings of the NATO Advanced Research Workshop on Comparative Time Series Analysis, held in Santa Fe, New Mexico, May 14-17, 1992

Author: Andreas S Weigend; Neil A Gershenfeld
Publisher: New York, NY : Routledge, Taylor & Francis Group, 2018. ©1994
Series: Proceedings volume in the Santa Fe Institute studies in the sciences of complexity, v. 15.
Edition/Format:   eBook : Document : Conference publication : EnglishView all editions and formats
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Genre/Form: Conference papers and proceedings
Electronic books
Congresses
Material Type: Conference publication, Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Andreas S Weigend; Neil A Gershenfeld
ISBN: 9780429492648 0429492642
OCLC Number: 1035762433
Description: 1 online resource (xvii, 643 pages) : illustrations.
Contents: Cover --
Half Title --
Title --
Copyright --
Contents --
Preface --
The Future of Time Series: Learning and Understanding --
Section I. Description of the Data Sets --
Lorenz-Like Chaos in NH3-FIR Lasers (Data Set A) --
Multi-Channel Physiological Data: Description and Analysis (Data Set B) --
Foreign Currency Dealing: A Brief Introduction (Data Set C) --
Whole Earth Telescope Observations of the White Dwarf Star (PG1159-035) (Data Set E) --
Baroque Forecasting: On Completing J. S. Bach's Last Fugue (Data Set F) --
Section II. Time Series Prediction --
Time Series Prediction by Using Delay Coordinate Embedding (Data Set A) --
Time Series Prediction by Using a Connectionist Network with Internal Delay Lines (Data Set A) --
Simple Architectures on Fast Machines: Practical Issues in Nonlinear Time Series Prediction (Data Sets C,D,F) --
Neural Net Architectures for Temporal Sequence Processing (Data Set C) --
Forecasting Probability Densities by Using Hidden Markov Models with Mixed States (Data Set D) --
Time Series Prediction by Using the Method of Analogues (Data Set A) --
Modeling Time Series by Using Multivariate Adaptive Regression Splines (MARS) (Data Sets A,B,C) --
Visual Fitting and Extrapolation (Data Set A) --
Does a Meeting in Santa Fe Imply Chaos? (Data Set A) --
Section III. Time Series Analysis and Characterization --
Exploring the Continuum Between Deterministic and Stochastic Modeling (Data Sets A,B,D) --
Estimating Generalized Dimensions and Choosing Time Delays: A Feist Algorithm (Data Sets A,D) --
Identifying and Quantifying Chaos by Using Information-Theoretic Functionals (Data Sets A,D,E) --
A Geometrical Statistic for Detecting Deterministic Dynamics (Data Sets A,B,C,D) --
Detecting Nonlinearity in Data with Long Coherence Times (Data Set E). Nonlinear Diagnostics and Simple Trading Rules for High-Frequency Foreign Exchange Rates (Data Set C) --
Noise Reduction by Local Reconstruction of the Dynamics (Data Set A) --
Section IV. Practice and Promise --
Large-Scale Linear Methods for Interpolation, Realization, and Reconstruction of Noisy, Irregularly Sampled Data --
Complex Dynamics in Physiology and Medicine --
Forecasting in Economics --
Finite-Dimensional Spatial Disorder: Description and Analysis --
Spatio-Temporal Patterns: Observations and Analysis --
Appendix: Accessing the Server --
Bibliography --
Index.
Series Title: Proceedings volume in the Santa Fe Institute studies in the sciences of complexity, v. 15.
Responsibility: editors Andreas S. Weigend, Neil A. Gershenfeld.

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