Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/17524
Title: Automatic extraction and identification of chart patterns towards financial forecast
Authors: Liu, JNK
Kwong, RWM
Keywords: CBR
Chart pattern extraction
Forecasting
Neural networks
Radial basis function network
Stock forecasting
Wavelet analysis
Issue Date: 2007
Publisher: Elsevier
Source: Applied soft computing, 2007, v. 7, no. 4, p. 1197-1208 How to cite?
Journal: Applied soft computing 
Abstract: Technical analysis of stocks mainly focuses on the study of irregularities, 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 requires considerable knowledge and experience of the stock market. It is also important for predicting stock market trends and turns. 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 promising, lack explanatory power or are dependent on domain experts. This paper presents an algorithm, PXtract to automate the recognition process of possible irregularities underlying the time series of stock data. It makes dynamic use of different time windows, and exploits the potential of wavelet multi-resolution analysis and radial basis function neural networks for the matching and identification of these irregularities. The study provides rooms for case establishment and interpretation, which are both important in investment decision making.
URI: http://hdl.handle.net/10397/17524
ISSN: 1568-4946
EISSN: 1872-9681
DOI: 10.1016/j.asoc.2006.01.007
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