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Computational Intelligence in Time Series Forecasting: Theory and Engineering Applications (Advances in Industrial Control) Hardcover – October 18, 2005

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Product Details

  • Series: Advances in Industrial Control
  • Hardcover: 372 pages
  • Publisher: Springer; 2005 edition (October 18, 2005)
  • Language: English
  • ISBN-10: 1852339489
  • ISBN-13: 978-1852339487
  • Product Dimensions: 9.2 x 6.1 x 0.9 inches
  • Shipping Weight: 1.6 pounds (View shipping rates and policies)
  • Average Customer Review: 3.0 out of 5 stars  See all reviews (2 customer reviews)
  • Amazon Best Sellers Rank: #4,710,157 in Books (See Top 100 in Books)

Editorial Reviews


From the reviews:

This is a monograph whose aim is of special and singular interest: to present systematic and comprehensive methods and techniques of computational intelligence and soft computing for solving forecasting and prediction problems … of time series. The book is designed to be largely self-contained and is devoted to offer researchers, practicing engineers, and applications-oriented professionals a reference volume and a valuable guide for the design, building and execution of forecasting and prediction experiments … . The entire monograph is sensibly structured … .

Zentralblatt MATH 1095 (2006) (Reviewer: Neculai Curteanu)

From the Back Cover

Foresight in an engineering enterprise can make the difference between success and failure and can be vital to the effective control of industrial systems. Forecasting the future from accumulated historical data is a tried and tested method in areas such as engineering finance. Applying time series analysis in the on-line milieu of most industrial plants has been more problematic because of the time and computational effort required. The advent of soft computing tools such as the neural network and the genetic algorithm offers a solution.

Chapter by chapter, Computational Intelligence in Time Series Forecasting harnesses the power of intelligent technologies individually and in combination. Examples of the particular systems and processes susceptible to each technique are investigated, cultivating a comprehensive exposition of the improvements on offer in quality, model building and predictive control, and the selection of appropriate tools from the plethora available; these include:

• forecasting electrical load, chemical reactor behaviour and high-speed-network congestion using fuzzy logic;

• prediction of airline passenger patterns and of output data for nonlinear plant with combination neuro-fuzzy networks;

• evolutionary modelling and anticipation of stock performance by the use of genetic algorithms.

Application-oriented engineers in process control, manufacturing, the production industries and research centres will find much to interest them in Computational Intelligence in Time Series Forecasting and the book is suitable for industrial training purposes. It will also serve as valuable reference material for experimental researchers.


Advances in Industrial Control aims to report and encourage the transfer of technology in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. The series offers an opportunity for researchers to present an extended exposition of new work in all aspects of industrial control.

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Most Helpful Customer Reviews

1 of 1 people found the following review helpful By Dekwo Kybon on July 4, 2008
Format: Hardcover
The book is a summary of equations. These equations aren't explained whatsoever. Not even the symbols used in those equations are explained.

If you understand this book, you shouldn't have bought it, because you probably allready understood everything that was in it.

It looks nice, but it's not.
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1 of 3 people found the following review helpful By Mohadig Widha Rousstia on February 18, 2006
Format: Hardcover
For the novice reader in this field of expertise this book is well suited. It does not mean that this book is not extensively explained, but the book is easy to understand since it is going step by step through many research result examples conducted in clear way. The author intended the book to be finished within approximately few weeks of intensively reading,and some prior knowledges. The presentation is awesome and includes up-to-date notions, namely Neural Network approach, Fuzzy Logic perspective and Evolutionary Computing as well. The Evolutionary Computing contains Genetic Algorithm, Genetic Programming, Evolutionary Strategy, Evolutionary Programming and Differential Evolution. The four last notions are left in concise for some reasons. Furthermore, the Hybrid method,i.e. Neuro-Fuzzy, is explained completely by many research results exploiting the robustness of Takagi-Sugeno fuzzy inference system applied with Mackey-Glass Chaotic time series and Wang data nonlinear inputs. The most recent developments are presented in some pages including Wavelet Networks, Fractal Based Network and Fuzzy Clustering.

However, this book is intended for postgraduate student, scientist and industrial workers in area of control, pattern recognition, robotic and many more.
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