Neural Network Systems Technology in the Analysis if Financial Time Series

  • Renate Sitte
  • Joaquin Sitte


As human nature strives for wealth and comfort in life, the ways of attaining wealth have changed through time. In the dark ages the focus was on turning material into gold, but nowadays the efforts go into managing and manipulating trades enabling profitable growth in different economic strata. For decades, financial time series predictions have been the target for profitable trade. Those who succeeded are reluctant to share their secrets. Thus successful applications of times series prediction techniques are unlikely to be found in the scholarly literature of the field. We cannot promise a pot of gold, but we will explain financial time series, with emphasis in using neural networks for their prediction, as a technique which has brought significant improvements not only to time series predictions but also enabled new technologies in many other areas. Neural Networks are particularly attractive for the prediction of financial time series because they do not require any kind of model knowledge about the relationships between variables [1]. In this chapter we shall review different approaches to financial time series. We present this material in a way that is understandable to a wide and interdisciplinary audience.


Neural Network Time Series Hide Layer Finite Impulse Response Feedforward Neural Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Kluwer Academic Publishers 2005

Authors and Affiliations

  • Renate Sitte
    • 1
  • Joaquin Sitte
    • 2
  1. 1.Faculty of Engineering and Information and TechnologyGriffith UniversityQueenslandAustralia
  2. 2.Faculty of Information TechnologyQueensland University of TechnologyBrisbaneAustralia

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