Chart patterns recognition and forecast using wavelet and radial basis function network

James N.K. Liu, Raymond W.M. Kwong, Feng Bo

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

6 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsMircea Gh. Negoita, Robert J. Howlett, Lakhmi C. Jain
PublisherSpringer Verlag
Pages564-571
Number of pages8
ISBN (Print)9783540232063
DOIs
Publication statusPublished - 2004

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3214
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

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