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Fuzzy Candlesticks Forecasting Using Pattern Recognition for Stock Markets

  • Conference paper
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International Joint Conference SOCO’16-CISIS’16-ICEUTE’16 (SOCO 2016, CISIS 2016, ICEUTE 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 527))

Abstract

This paper presents a prediction system based on fuzzy modeling of Japanese candlesticks. The prediction is performed using the pattern recognition methodology and applying a lazy and nonparametric classification technique, k-Nearest Neighbours (k-NN). The Japanese candlestick chart summarizes the trading period of a commodity with only 4 parameters (open, high, low and close). The main idea of the decision system implemented in this article is to predict with accuracy, based on this vague information from previous sessions, the performance of future sessions. Therefore, investors could have valuable information about the next session and set their investment strategies.

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Author information

Authors and Affiliations

  1. Computer Science Faculty, University Complutense of Madrid, 28040, Madrid, Spain

    Rodrigo Naranjo & Matilde Santos

Authors
  1. Rodrigo Naranjo
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  2. Matilde Santos
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Corresponding author

Correspondence to Rodrigo Naranjo .

Editor information

Editors and Affiliations

  1. Computational Intelligence Group, University of the Basque Country (UPV/EHU), San Sebastian, Spain

    Manuel Graña

  2. University of the Basque Country, Vitoria, Spain

    José Manuel López-Guede

  3. Computational Intelligence Group, University of the Basque Country (UPV/EHU), San Sebastian, Spain

    Oier Etxaniz

  4. Department of Civil Engineering, University of Burgos, Burgos, Spain

    Álvaro Herrero

  5. University of Salamanca, Salamanca, Spain

    Héctor Quintián

  6. University of Salamanca, Salamanca, Spain

    Emilio Corchado

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Naranjo, R., Santos, M. (2017). Fuzzy Candlesticks Forecasting Using Pattern Recognition for Stock Markets. In: Graña, M., López-Guede, J.M., Etxaniz, O., Herrero, Á., Quintián, H., Corchado, E. (eds) International Joint Conference SOCO’16-CISIS’16-ICEUTE’16. SOCO CISIS ICEUTE 2016 2016 2016. Advances in Intelligent Systems and Computing, vol 527. Springer, Cham. https://doi.org/10.1007/978-3-319-47364-2_31

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  • DOI: https://doi.org/10.1007/978-3-319-47364-2_31

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-47363-5

  • Online ISBN: 978-3-319-47364-2

  • eBook Packages: EngineeringEngineering (R0)

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