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

TL;DR

This paper explores how relational symbolic data mining techniques, traditionally used in non-numeric fields, can be effectively applied to financial time series analysis, offering an alternative to dominant statistical and neural network methods.

Contribution

It introduces the adaptation of relational data mining methods for financial data, demonstrating their potential benefits in stock market forecasting.

Findings

Relational symbolic methods can improve financial data analysis.

Symbolic techniques complement existing statistical approaches.

Potential for enhanced accuracy in financial predictions.

Abstract

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.