Investigation of the latent space of stock market patterns with genetic programming
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- @InProceedings{Ha:2018:GECCO,
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author = "Sungjoo Ha and Sangyeop Lee and Byung-Ro Moon",
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title = "Investigation of the latent space of stock market
patterns with genetic programming",
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booktitle = "GECCO '18: Proceedings of the Genetic and Evolutionary
Computation Conference",
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year = "2018",
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editor = "Hernan Aguirre and Keiki Takadama and
Hisashi Handa and Arnaud Liefooghe and Tomohiro Yoshikawa and
Andrew M. Sutton and Satoshi Ono and Francisco Chicano and
Shinichi Shirakawa and Zdenek Vasicek and
Roderich Gross and Andries Engelbrecht and Emma Hart and
Sebastian Risi and Ekart Aniko and Julian Togelius and
Sebastien Verel and Christian Blum and Will Browne and
Yusuke Nojima and Tea Tusar and Qingfu Zhang and
Nikolaus Hansen and Jose Antonio Lozano and
Dirk Thierens and Tian-Li Yu and Juergen Branke and
Yaochu Jin and Sara Silva and Hitoshi Iba and
Anna I Esparcia-Alcazar and Thomas Bartz-Beielstein and
Federica Sarro and Giuliano Antoniol and Anne Auger and
Per Kristian Lehre",
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isbn13 = "978-1-4503-5618-3",
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pages = "1254--1261",
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address = "Kyoto, Japan",
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DOI = "doi:10.1145/3205455.3205493",
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publisher = "ACM",
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publisher_address = "New York, NY, USA",
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month = "15-19 " # jul,
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organisation = "SIGEVO",
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keywords = "genetic algorithms, genetic programming",
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abstract = "We suggest a use of genetic programming for
transformation from a vector space to an understandable
graph representation, which is part of a project to
inspect the latent space in matrix factorization. Given
a relation matrix, we can apply standard techniques
such as non-negative matrix factorization to extract
low dimensional latent space in vector representation.
While the vector representation of the latent space is
useful, it is not intuitive and hard to interpret. The
transformation with the help of genetic programming
allows us to better understand the underlying latent
structure. Applying the method in the context of a
stock market, we show that it is possible to recover
the tree representation of technical patterns from a
relation matrix. Leveraging the properties of the
vector representations, we are able to find patterns
that correspond to cluster centres of technical
patterns. We further investigate the geometry of the
latent space.",
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notes = "Also known as \cite{3205493} GECCO-2018 A
Recombination of the 27th International Conference on
Genetic Algorithms (ICGA-2018) and the 23rd Annual
Genetic Programming Conference (GP-2018)",
- }
Genetic Programming entries for
Sungjoo Ha
Sangyeop Lee
Byung-Ro Moon
Citations