Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/70077
Title: Chart patterns recognition and forecast using wavelet and radial basis function network
Authors: Liu, JNK
Kwong, RWM
Feng, BO
Issue Date: 2004
Publisher: Springer
Source: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics), 2004, v. 3214, p. 564-571 How to cite?
Journal: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics) 
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.
Description: 8th International Conference on Knowledge-Based Intelligent Information & Engineering Systems (KES 2004), Wellington, New Zealand, 22-24 September 2004
URI: http://hdl.handle.net/10397/70077
ISBN: 978-3-540-23206-3
978-3-540-30133-2
ISSN: 0302-9743
EISSN: 1611-3349
DOI: 10.1007/978-3-540-30133-2_74
Appears in Collections:Conference Paper

Access
View full-text via PolyU eLinks SFX Query
Show full item record

Page view(s)

31
Citations as of May 21, 2018

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.