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Multi-dimensional pattern discovery in financial time series using sax-ga with extended robustness

Published: 06 July 2013 Publication History

Abstract

This paper proposes a new Multi-Dimensional SAX-GA approach to pattern discovery using genetic algorithms (GA). The approach is capable of discovering patterns in multi-dimensional financial time series. First, the several dimensions of data are converted to a Symbolic Aggregate approXimation (SAX) representation, which is, then, feed to a GA optimization kernel. The GA searches for profitable patterns occurring simultaneously in the multi-dimensional time series. Based on the patterns found, the GA produces more robust investment strategies, since the simultaneity of patterns on different dimensions of the data, reinforces the strength of the trading decisions implemented. The proposed approach was tested using stocks from S&P500 index, and is compared to previous reference works of SAX-GA and to the Buy & Hold (B&H) classic investment strategy.

References

[1]
A. Canelas, R. Neves, and N. Horta. A new SAX-GA methodology applied to investment strategies optimization. Proc. 14th Inter. conf. on Genetic Evol. Comp. (GECCO'12), pages 1055--1062. July 2012
[2]
A. Canelas, R. Neves, and N. Horta. A SAX-GA approach to evolve investment strategies on financial markets based on pattern discovery techniques. Expert Syst. With Appl. 40 (5):1579--1590. April 2013.
[3]
E. Keogh, J. Lin, and A. Fu. HOT SAX: Efficiently Finding the Most Unusual Time Series Subsequence. Proc. 5th IEEE Inter. Conf. Data Min. (ICDM '05), pages 226--233. November 2005.
[4]
A. Mcgovern, D.H. Rosendahl, R.A. Brown, and K.K. Droegemeier. Identifying predictive multi-dimensional time series motifs: an application to severe weather prediction. Data Min. Knowl. Discov. 22(1--2):232--258. January 2011.
[5]
Y. Tanaka, K. Iwamoto, and K. Uehara. Discovery of Time-Series Motif from Multi-Dimensional Data Based on MDL Principle. Mach. Learn. 58(2--3):269--300. February 2005.

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  • (2021)Soil-Moisture-Sensor-Based Automated Soil Water Content Cycle Classification With a Hybrid Symbolic Aggregate Approximation AlgorithmIEEE Internet of Things Journal10.1109/JIOT.2021.30683798:18(14003-14012)Online publication date: 15-Sep-2021
  • (2019)Adaptive detection of FOREX repetitive chart patternsPattern Analysis and Applications10.1007/s10044-019-00862-8Online publication date: 4-Dec-2019
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  1. Multi-dimensional pattern discovery in financial time series using sax-ga with extended robustness

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    Published In

    cover image ACM Conferences
    GECCO '13 Companion: Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
    July 2013
    1798 pages
    ISBN:9781450319645
    DOI:10.1145/2464576
    • Editor:
    • Christian Blum,
    • General Chair:
    • Enrique Alba
    Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 06 July 2013

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    Author Tags

    1. financial market
    2. genetic algorithm
    3. multi-dimensional time series analysis
    4. pattern discovery
    5. sax representation
    6. time series

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    GECCO '13
    Sponsor:
    GECCO '13: Genetic and Evolutionary Computation Conference
    July 6 - 10, 2013
    Amsterdam, The Netherlands

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

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    Cited By

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    • (2023)Mutual Recall Between Onomatopoeia and Motion Using Doll Play CorpusDistributed, Ambient and Pervasive Interactions10.1007/978-3-031-34668-2_18(265-280)Online publication date: 9-Jul-2023
    • (2021)Soil-Moisture-Sensor-Based Automated Soil Water Content Cycle Classification With a Hybrid Symbolic Aggregate Approximation AlgorithmIEEE Internet of Things Journal10.1109/JIOT.2021.30683798:18(14003-14012)Online publication date: 15-Sep-2021
    • (2019)Adaptive detection of FOREX repetitive chart patternsPattern Analysis and Applications10.1007/s10044-019-00862-8Online publication date: 4-Dec-2019
    • (2018)SAX/GA CPU ApproachParallel Genetic Algorithms for Financial Pattern Discovery Using GPUs10.1007/978-3-319-73329-6_4(33-44)Online publication date: 4-Feb-2018
    • (2018)IntroductionParallel Genetic Algorithms for Financial Pattern Discovery Using GPUs10.1007/978-3-319-73329-6_1(1-3)Online publication date: 4-Feb-2018
    • (2015)An investigation into the recurring patterns of forex time series data2015 IEEE International Symposium on Robotics and Intelligent Sensors (IRIS)10.1109/IRIS.2015.7451631(313-317)Online publication date: Oct-2015
    • (2015)Technical Indicators for Forex Forecasting: A Preliminary StudyAdvances in Swarm and Computational Intelligence10.1007/978-3-319-20469-7_11(87-97)Online publication date: 2-Jun-2015

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