Time Series Prediction: Forecasting The Future And Understanding The Past (Proceedings Volume, Santa Fe Institute Studies in the Sciences of Complexity) (Paperback)

by Andreas S. Weigend (Author), Neil A. Gershenfeld (Author), EDITOR * (Editor) "Throughout scientific research, measured time series are the basis for characterizing an observed system and for predicting its future behavior..." (more)
Key Phrases: average pulsation period, chaotic data sets, exponential trace memory, Times Series Prediction, Sciences of Complexity, Andreas Weigend (more...)

Editorial Reviews

About the Author

Neil Gershenfeld is the Director of MIT’s Center for Bits and Atoms, and the former director of its famed Media Lab. The author of numerous technical publications, patents, and books, including When Things Start to Think, he has been featured in media such as the New York Times, The Economist, CNN, and PBS. He lives in Somerville, Massachusetts.

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Product Details Paperback: 672 pages Publisher: Westview Press (November 19, 1993) Language: English ISBN-10: 0201626020 ISBN-13: 978-0201626025 Product Dimensions: 9.1 x 6.4 x 1 inches Shipping Weight: 2 pounds (View shipping rates and policies) Average Customer Review: 5.0 out of 5 stars See all reviews (1 customer review) Amazon.com Sales Rank: #964,577 in Books (See Bestsellers in Books)

Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!

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Inside This Book (learn more)

First Sentence:
Throughout scientific research, measured time series are the basis for characterizing an observed system and for predicting its future behavior. Read the first page

Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
average pulsation period, chaotic data sets, exponential trace memory, torus series, marginal redundancy, nonlinear threshold models, universal dynamical system, synaptic filter, foreign exchange series, surrogate time series, nonlinear diagnostics, truncated splines, linear redundancy, physiological time series, time series competition, iterated prediction, gamma memory, global linear models, surrogate data, hourly series, delay coordinate vector, reconstructed state space, temporal backpropagation, long coherence times, circular autocorrelation

Key Phrases - Capitalized Phrases (CAPs): (learn more)
Times Series Prediction, Sciences of Complexity, Andreas Weigend, Santa Fe Institute, Monte Carlo, James Theiler, Connection Machine, Astrophysical Journal, New York, Palo Alto, University of Colorado, Whole Earth Telescope, Coyote Hill Road, Subba Rao, The Future of Time Series

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Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!

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Citations (learn more)

This book cites 100 books:

• Advances in Neural Information Processing Systems 12 by Sara A. Solla on 6 pages
• Ergodic Theory (Cambridge Studies in Advanced Mathematics) by Karl E. Petersen in Back Matter (1), Back Matter (2), and Back Matter (3)
• Measures of Complexity and Chaos (NATO Science Series: B:) by Neal B. Abraham in Back Matter (1), Back Matter (2), and Back Matter (3)
• Computer and Music by Harry B. Lincoln in Back Matter (1), and Back Matter (2)
• Los Alamos (Spanish Edition) by Martin Cruz Smith in Front Matter, and Back Matter

See all 100 books this book cites

 

7 books cite this book:

• Pattern Formation in the Physical and Biological Sciences (Santa Fe Institute Studies in the Sciences of Complexity, Lecture Notes, Vol 5) by H. Frederick Nijhout on 4 pages
• Pattern Formation In The Physical And Biological Sciences (Santa Fe Institute Studies in the Sciences of Complexity Lecture Notes) by H. Frederick Nijhout on page 420, page 424, and page 428
• The Economic World View: Studies in the Ontology of Economics by Uskali Mäki on page 356
• Thinking About Biology (Santa Fe Institute Studies in the Sciences of Complexity : Lecture Notes, Vol) by Wilfred Stein in Front Matter
• The Evolving Rationality of Rational Expectations: An Assessment of Thomas Sargent's Achievements by Esther-Mirjam Sent in Back Matter

See all 7 books citing this book

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Books on Related Topics (learn more)

Nonlinear Time Series Analysis by Holger Kantz

Discusses:

• time series competition
• iterated prediction
• surrogate data

Complex Systems Science in Biomedicine by Thomas S. Deisboeck

Discusses:

• physiological time series
• reconstructed state space
• Santa Fe Institute

Kalman Filtering and Neural Networks by Simon Haykin

Discusses:

• iterated prediction
• surrogate data
• delay coordinate vector

The Industrial Electronics Handbook by J. David Irwin

Discusses:

• iterated prediction
• gamma memory
• temporal backpropagation

Coping with Chaos by Edward Ott

Discusses:

• surrogate data
• delay coordinate vector
• Sciences of Complexity

A Field Guide to Dynamical Recurrent Networks by John F. Kolen

Discusses:

• iterated prediction
• gamma memory
• recurrent hidden units

Nonlinear Time Series Analysis by Holger Kantz Discusses: time series competition iterated prediction surrogate data Complex Systems Science in Biomedicine by Thomas S. Deisboeck Discusses: physiological time series reconstructed state space Santa Fe Institute Kalman Filtering and Neural Networks by Simon Haykin Discusses: iterated prediction surrogate data delay coordinate vector The Industrial Electronics Handbook by J. David Irwin Discusses: iterated prediction gamma memory temporal backpropagation Loading...

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Most Helpful Customer Reviews 3 of 3 people found the following review helpful: 5.0 out of 5 stars Excellent book in case studies, November 29, 2007 By  D. Hundley (Walla Walla, WA United States) - See all my reviews (REAL NAME)    I am suprised that no one has reviewed this book- I have found it very useful to have on my bookshelf. The book is a summary of a time series forecasting competition that was held a few years ago. The competition used four different kinds of time series (for example, one data set was chaotic from measurements of a laser, and another was a multidimensional physiological times series of heart beats and respiration, etc.). The strength of the book is that it represents several ways to approach real time series prediction strategies in a concrete way - Invaluable, especially to researchers who may be just beginning. FYI- I think the data is available on the world wide web, so it is possible to perform the analysis that is described in the book.		Ad feedback   Search Customer Reviews Only search this product's reviews

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Product Details

• Paperback: 672 pages
• Publisher: Westview Press (November 19, 1993)
• Language: English
• ISBN-10: 0201626020
• ISBN-13: 978-0201626025
• Product Dimensions: 9.1 x 6.4 x 1 inches
• Shipping Weight: 2 pounds (View shipping rates and policies)
• Average Customer Review: 5.0 out of 5 stars See all reviews (1 customer review)
• Amazon.com Sales Rank: #964,577 in Books (See Bestsellers in Books)

Customer Reviews

Most Helpful Customer Reviews

3 of 3 people found the following review helpful:

5.0 out of 5 stars Excellent book in case studies, November 29, 2007

By	D. Hundley (Walla Walla, WA United States) - See all my reviews (REAL NAME)

I am suprised that no one has reviewed this book- I have found it very useful to have on my bookshelf. The book is a summary of a time series forecasting competition that was held a few years ago. The competition used four different kinds of time series (for example, one data set was chaotic from measurements of a laser, and another was a multidimensional physiological times series of heart beats and respiration, etc.).

The strength of the book is that it represents several ways to approach real time series prediction strategies in a concrete way - Invaluable, especially to researchers who may be just beginning.

FYI- I think the data is available on the world wide web, so it is possible to perform the analysis that is described in the book.