Antonio Canelas
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.
- 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 Google Scholar
Digital Library
- 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. Google ScholarDigital Library
- 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. Google ScholarDigital Library
- 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. Google ScholarDigital Library
- 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. Google ScholarDigital Library
Index Terms
- Multi-dimensional pattern discovery in financial time series using sax-ga with extended robustness
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