BIBLIO is the largest independent book marketplace in the world, with over 100 million books.

Skip to content

High Performance Computing Applied to Nonlinear Time Series Analysis

High Performance Computing Applied to Nonlinear Time Series Analysis

High Performance Computing Applied to Nonlinear Time Series Analysis
Stock photo: cover may vary

High Performance Computing Applied to Nonlinear Time Series Analysis Paperback - 2010

by Marín Carrión, Ismael

Add to wish list
  • Used
  • Good
  • Paperback
Used - Good

Description

paperback. Good. Access codes and supplements are not guaranteed with used items. May be an ex-library book.
Ask the seller a question Add to wish list
A$186.33
Free Delivery within USA
Standard delivery: 7 to 14 days
More delivery options
Dropship order
Ships from Bonita (California, United States)

Details

  • Title High Performance Computing Applied to Nonlinear Time Series Analysis
  • Author Marín Carrión, Ismael
  • Binding Paperback
  • Condition Used - Good
  • Pages 184
  • Volumes 1
  • Language ENG
  • Publisher LAP Lambert Academic Publishing
  • Publication date 2010-05
  • Bookseller's Inventory # 3838365879.G
  • ISBN 9783838365879 / 3838365879
  • Weight 0.62 lbs (0.28 kg)
  • Dimensions 9 x 6 x 0.42 in (22.86 x 15.24 x 1.07 cm)
  • Category Computers - General Information
  • Quantity available 1

About Bonita California, United States

Biblio member since 2020

Terms of Sale: 30 day return guarantee, with full refund including original shipping costs for up to 30 days after delivery if an item arrives misdescribed or damaged.

Browse books from Bonita

Reader reviews for High Performance Computing Applied to Nonlinear Time Series Analysis

From the publisher

Many applications of science and engineering, e.g. in physics, biology, economics or meteorology, are determined by dynamical systems. These systems evolve over time and then generate a set of data spaced in time, called time series. The analysis of time series from real systems, in terms of nonlinear dynamics, is the most direct link between chaos theory and the real world. Very useful information for making predictions about dynamical systems is extracted from the analysis of these time series. Since many of these applications must provide a real time response, it is necessary for analysis and prediction to be performed on a reasonable time scale. High Performance Computing gives a feasible solution to this problem, which enables it to be solved in an efficient manner. Nowadays, parallel computing is one of the most appropriate ways of obtaining important computational power. Thus, a set of high performance algorithms has been developed in this Thesis for both nonlinear time series analysis and, then, prediction. Finally, the Thesis proposes a method of time series modeling and predicting based on stochastic subspace system identification.
tracking-