research-article

Fast Knowledge Discovery in Time Series with GPGPU on Genetic Programming

Authors:

Byung-Ro MoonAuthors Info & Claims

GECCO '15: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation

Pages 1159 - 1166

https://doi.org/10.1145/2739480.2754669

Published: 11 July 2015 Publication History

Abstract

We tackle the problem of knowledge discovery in time series data using genetic programming and GPGPUs. Using genetic programming, various precursor patterns that have certain attractive qualities are evolved to predict the events of interest. Unfortunately, evolving a set of diverse patterns typically takes huge execution time, sometimes longer than one month for this case. In this paper, we address this problem by proposing a parallel GP framework using GPGPUs, particularly in the context of big financial data. By maximally exploiting the structure of the nVidia GPGPU platform on stock market time series data, we were able see more than 250-fold reduction in the running time.

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Cited By

Ha SLee SMoon BTakadama K(2018)Investigation of the latent space of stock market patterns with genetic programmingProceedings of the Genetic and Evolutionary Computation Conference10.1145/3205455.3205493(1254-1261)Online publication date: 2-Jul-2018
https://dl.acm.org/doi/10.1145/3205455.3205493
Ha SMoon B(2018)Finding attractive technical patterns in cryptocurrency marketsMemetic Computing10.1007/s12293-018-0252-y10:3(301-306)Online publication date: 9-Mar-2018
https://doi.org/10.1007/s12293-018-0252-y
Ha SLee SMoon BNeumann FSutton A(2016)Inspecting the Latent Space of Stock Market Data with Genetic ProgrammingProceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion10.1145/2908961.2909004(63-64)Online publication date: 20-Jul-2016
https://dl.acm.org/doi/10.1145/2908961.2909004

Index Terms

Fast Knowledge Discovery in Time Series with GPGPU on Genetic Programming
1. Applied computing
2. Computer systems organization
  1. Architectures
    1. Parallel architectures
      1. Interconnection architectures
      2. Multiple instruction, multiple data

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cover image ACM Conferences

GECCO '15: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation

July 2015

1496 pages

ISBN:9781450334723

DOI:10.1145/2739480

Editor:
Sara Silva
Universidade de Lisboa, Portugal
,
General Chair:
Anna I. Esparcia-Alcázar
Universitat Politècnica de València, Spain

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 11 July 2015

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GECCO '15

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SIGEVO

GECCO '15: Genetic and Evolutionary Computation Conference

July 11 - 15, 2015

Madrid, Spain

Acceptance Rates

GECCO '15 Paper Acceptance Rate 182 of 505 submissions, 36%;

Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

Ha SLee SMoon BTakadama K(2018)Investigation of the latent space of stock market patterns with genetic programmingProceedings of the Genetic and Evolutionary Computation Conference10.1145/3205455.3205493(1254-1261)Online publication date: 2-Jul-2018
https://dl.acm.org/doi/10.1145/3205455.3205493
Ha SMoon B(2018)Finding attractive technical patterns in cryptocurrency marketsMemetic Computing10.1007/s12293-018-0252-y10:3(301-306)Online publication date: 9-Mar-2018
https://doi.org/10.1007/s12293-018-0252-y
Ha SLee SMoon BNeumann FSutton A(2016)Inspecting the Latent Space of Stock Market Data with Genetic ProgrammingProceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion10.1145/2908961.2909004(63-64)Online publication date: 20-Jul-2016
https://dl.acm.org/doi/10.1145/2908961.2909004

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