Genetic Algorithm Search for Predictive Patterns in Multidimensional Time Series
Arnold Polanski
School of Management and Economics
Queen's University of Belfast
25 University Square
Belfast BT7 1NN, United Kingdom
a.polanski@qub.ac.uk
Abstract
Based on an algorithm for pattern matching in character strings, a pattern matching machine is implemented that searches for occurrences of patterns in multidimensional time series. Before the search process takes place, time series data is encoded in user-designed alphabets. The patterns, on the other hand, are formulated as regular expressions that are composed of letters from these alphabets and operators. Furthermore, a genetic algorithm is developed to breed patterns that maximize a user-defined fitness function. In an application to financial data, it is shown that patterns bred to predict high exchange rates volatility in training samples retain statistically significant predictive power in validation samples.
