An intelligent system for financial time series prediction combining dynamical systems theory, fractal theory, and statistical methods

Abstract

Describes a computer program that can be considered as an intelligent system for the domain of financial time series prediction. The computer program is an implementation of a new algorithm for discovering mathematical models for financial time series prediction, combining artificial intelligence methodology with dynamical systems theory, fractal theory and statistical methods. Given a financial time series for an specific problem, the intelligent system develops mathematical models for the problem based on the geometry of the data, using three different approaches. First, the computer program develops regression models for the time series using traditional statistical methods, then it develops nonlinear mathematical models based on dynamical systems theory and chaos theory, and finally it develops fractal mathematical models based on the theory of fractal geometry. The intelligent system then analyzes all of the mathematical models obtained before making a selection of the model that gives the best prediction for the financial time series. This selection is done by the intelligent system using a combination of heuristics and calculations that are contained in the knowledge base. An intelligent system that can learn models from financial data would be very useful in practice in making the task of prediction easier and less time-consuming.