Computational Intelligence in Time Series Forecasting: Theory and Engineering Applications by Ajoy K. PalitComputational Intelligence in Time Series Forecasting: Theory and Engineering Applications by Ajoy K. Palit

Computational Intelligence in Time Series Forecasting: Theory and Engineering Applications

byAjoy K. Palit, Dobrivoje Popovic

Paperback | October 21, 2010

Pricing and Purchase Info


Earn 1,375 plum® points

Prices and offers may vary in store


In stock online

Ships free on orders over $25

Not available in stores


Foresight can be crucial in process and production control, production-and-resources planning and in management decision making generally. Although forecasting the future from accumulated historical data has become a standard and reliable method in production and financial engineering, as well as in business and management, the use of 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 intelligent computational technologies such as the neural network and the genetic algorithm promotes the efficient solution of on-line forecasting problems. Their most outstanding successes include:

  • prediction of nonlinear time series and the nonlinear combination of forecasts using neural networks;
  • prediction of chaotic time series and of output data for second-order nonlinear plant using fuzzy logic.

The power of intelligent technologies applied individually and in combination, has created advanced forecasting methodologies, exemplified inComputational Intellingence in Time Series Forecastingby particular systems and processes. The authors give 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 using such examples as:

  • forecasting of electrical load and of output data for nonlinear plant with neuro-fuzzy networks;
  • temperature prediction and correction in pyrometer reading, tool-wear monitoring and materials property prediction using hybrid intelligent technologies;
  • evolutionary training of neuro-fuzzy networks by the use of genetic algorithms and prediction of chaotic time series;
  • isolated use of neural networks and fuzzy logic in the nonlinear combination of traditional forecasts of temperature series obtained from a pilot-scale chemical reactor with temporarily disconnected controller.

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

Title:Computational Intelligence in Time Series Forecasting: Theory and Engineering ApplicationsFormat:PaperbackDimensions:372 pages, 23.5 × 15.5 × 0.17 inPublished:October 21, 2010Publisher:Springer-Verlag/Sci-Tech/TradeLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:1849969701

ISBN - 13:9781849969703

Look for similar items by category:


Table of Contents

Part I: Introduction Computational Intelligence: An Introduction Traditional Problem Definition Part II: Basic Intelligent Computational Technologies Neural Networks Approach Fuzzy Logic Approach Evolutionary Computation Part III: Hybrid Computational Technologies Neuro-fuzzy Approach Transparent Fuzzy/Neuro-fuzzy Modeling Evolving Neural and Fuzzy Systems Adaptive Genetic Algorithms Part IV: Recent Developments The State of the Art and Development Trends

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)