Symbolic Methodology in Numeric Data Mining: Relational Techniques for Financial Applications

Details Symbolic Methodology in Numeric Data Mining: Relational Techniques for Financial Applications Symbolic Methodology in Numeric Data Mining: Relational Techniques for Financial Applications

2002-08-15

arxiv.org/abs/cs/0208022v1

Computer Science

Computational Engineering, Finance, and Science

20 pages, 1 figure, 16 tables

Scientific paper

Currently statistical and artificial neural network methods dominate in financial data mining. Alternative relational (symbolic) data mining methods have shown their effectiveness in robotics, drug design and other applications. Traditionally symbolic methods prevail in the areas with significant non-numeric (symbolic) knowledge, such as relative location in robot navigation. At first glance, stock market forecast looks as a pure numeric area irrelevant to symbolic methods. One of our major goals is to show that financial time series can benefit significantly from relational data mining based on symbolic methods. The paper overviews relational data mining methodology and develops this techniques for financial data mining.

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