Basic Study on the Classification of Time Series Data Using a Frequency Integrated Spherical Hidden Markov Self Organizing Map

Gen Niina; Hiroshi Dozono; Kazuhiro Muramatsu

doi:10.20965/jaciii.2015.p0212

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JACIII Vol.19 No.2 pp. 212-216

doi: 10.20965/jaciii.2015.p0212

(2015)

Paper:

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Basic Study on the Classification of Time Series Data Using a Frequency Integrated Spherical Hidden Markov Self Organizing Map

Gen Niina, Hiroshi Dozono, and Kazuhiro Muramatsu

Faculty of Science and Engineering, Saga University
1-Honjyo, Saga 840-8502, Japan

Received:

May 13, 2014

Accepted:

November 21, 2014

Published:

March 20, 2015

Keywords:

self organizing map, hidden markov model, stock price movements

Abstract

The rapid progress in and the expanding complexity of information and technology systems have made data analysis increasingly relevant. Data having a variety of elements are complex, and making very difficult to evaluate a state of a model from observed data generated probabilistically by the model. To evaluate these hidden states, we propose Spherical-Self Organizing Map (S-SOM) with a Hidden Markov Model (HMM) that infers such hidden states.

Cite this article as:

G. Niina, H. Dozono, and K. Muramatsu, “Basic Study on the Classification of Time Series Data Using a Frequency Integrated Spherical Hidden Markov Self Organizing Map,” J. Adv. Comput. Intell. Intell. Inform., Vol.19 No.2, pp. 212-216, 2015.

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References

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[4] G. Niina and H. Dozono, “The Spherical Hidden Markov Self Organizing Map for Learning Time Series Data,” ICANN Part 1, pp. 563-570, 2012.
[5] D. Nakatsuka and M. Oyabu, “Application of Spherical SOM in Clustering,” Proc. Workshop on Self-Organizing Maps (WSOM’03), pp. 203-207, 2003.
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This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

[1] [1] A. Takahashi, S. Kanaya, M. Kinouchi, T. Kozuki, and T. Ikemura, “Informatics for unrevealing hidden genome signatures,” Genome Research, Vol.13, pp. 693-702, 2003.

[2] [2] H. Dozono, T. Kabashima, et. al., “Visualization of the Packet Flows using Self Organizing Maps,” WSEAS Trans. on Information Science & Applications, Issue.1, Vol.7, pp. 132-141, 2010.

[3] [3] T. Akuts, “Mathematical Models and Algorithms in Bioinformatics,” A Series of Algorithm Science, Vol.12, Kyoritsu Shuppan, 2007.

[4] [4] G. Niina and H. Dozono, “The Spherical Hidden Markov Self Organizing Map for Learning Time Series Data,” ICANN Part 1, pp. 563-570, 2012.

[5] [5] D. Nakatsuka and M. Oyabu, “Application of Spherical SOM in Clustering,” Proc. Workshop on Self-Organizing Maps (WSOM’03), pp. 203-207, 2003.

[6] [6] N. Yamaguchi, “Self Organizing Hidden Markov Models,” Proc. of 17th Int. Conf. on Neural Information Processing, Models and Application (ICONIP 2010), LNCS6444, Springer, 2010.

[7] [7] R. Jaziri, M. Lebbah, and et. al., “SOS-HMM Self Organizing Structure of Hidden Markov Model,” Artificial Neural Networks and Machine Learning -- ICANN 2011, LNCS6792, Springer, 2011.