Chart Patterns Recognition and Forecast Using Wavelet and Radial Basis Function Network

  • James N. K. Liu
  • Raymond W. M. Kwong
  • Feng Bo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3214)


Technical analysis mainly focuses on analyzing the chart patterns, which is a non-trivial task. Because one time scale alone cannot be applied to all analytical processes, the identification of typical patterns on a stock price chart requires considerable knowledge and experience. The last two decades has seen attempts to solve such non-linear financial forecasting problems using AI technologies such as neural networks, fuzzy logic, genetic algorithms and expert systems but these, although accurate, lack explanatory power or are dependent on domain experts. This paper introduces a case based reasoning (CBR) system that provides an explainable method of financial forecasting [4] that is not dependent on the inputs of domain experts. This study proposes an algorithm, PXtract, which identifies and analyses possible chart patterns, makes dynamic use of different time windows, and introduces a wavelet multi-resolution analysis incorporated within a radial basis function neural network (RBFNN) matching method that can be used to automate the chart pattern matching process.


Hide Node Wave Pattern Radial Basis Function Neural Network Radial Basis Function Network Case Base Reasoning 
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

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • James N. K. Liu
    • 1
  • Raymond W. M. Kwong
    • 1
  • Feng Bo
    • 1
  1. 1.Department of ComputingHong Kong Polytechnic University 

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