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HMM Training by Using a Self-Organizing Map for Time Series Prediction

Published: 24 February 2017 Publication History

Abstract

For individual investors to analyze changes in stock prices and foreign exchange rates to predict future trends is an extremely difficult task. In order to enable such predictions based on the analysis of time series data sets, this research proposes a method combining a Self-Organizing Map with a Hidden Markov Model and provides evidence for its usefulness in making predictions.

References

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Gen Niina, Kazuhiro Muramatsu, Hiroshi Dozono, Tatsuya Chuuto, "The data analysis of stock market using a Frequency Integrated Spherical Hidden Markov Self Organizing Map", ICSIIT 2015, 4th International Conference on Soft Computing, Intelligent System and Information Technology, March 11-14, 2015 /Bari, Indonesia, pp. 195--204, 2015
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Gen Niina, Kazuhiro Muramatsu, Hiroshi Dozono, "The Frequency Integrated Spherical Hidden Markov Self Organizing Map for Learning Time Series Data", ISIS2013, The International Symposium on Advanced Intelligent Systems, Daejeon, Korea, pp. 362--370, 2013.
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cover image ACM Other conferences
ICMLC '17: Proceedings of the 9th International Conference on Machine Learning and Computing
February 2017
545 pages
ISBN:9781450348171
DOI:10.1145/3055635
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 ACM 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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  • Southwest Jiaotong University

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 February 2017

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Author Tags

  1. Neural network
  2. Self-Organizing map
  3. Time series data
  4. hidden markov modeling

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