JSM 2004 - Toronto

Abstract #300308

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Activity Number: 253
Type: Invited
Date/Time: Tuesday, August 10, 2004 : 2:00 PM to 3:50 PM
Sponsor: Business and Economics Statistics Section
Abstract - #300308
Title: An Analysis of Genetic Algorithms for Time Series Pattern Discovery
Author(s): Vasant Dhar*+
Companies: New York University
Address: Stern School of Business, Information Systems Department, New York, NY, 10012-1126,
Keywords: genetic algorithms ; automated pattern discovery ; machine learning
Abstract:

Genetic algorithms offer a powerful mechanism for learning from data, based on ideas from Darwinian Natural Selection. I will describe a genetic algorithm called GLOWER and its properties, and some results from using it to learn patterns from financial time series data. In particular, I shall describe the representation it uses for learning patterns, and results relative to those obtained via several other machine learning methods on a specific dataset. I shall also describe real-world experiences from using learned patterns to manage a global financial futures fund completely systematically. The latter are research in progress, including conjectures on when and why learning algorithms tend to "overfit" time series data.

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Revised March 2004