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Fuzzy c-Means Clustering in the Presence of Noise Cluster for Time Series Analysis

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Modeling Decisions for Artificial Intelligence (MDAI 2005)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3558))

Abstract

Cluster analysis for time series is becoming increasingly important in many real applications. Clustering plays an important role in extracting information from the noise in economic and financial time series. In this paper we consider the use of fuzzy c-means clustering method in the context of econometric analysis of time-series data. We discuss and demonstrate a methodology for model identification and estimation that is based on the fuzzy c-means algorithm in the presence of noise cluster that is widely used in the context of pattern recognition. The effect of noise on time-series prediction is important to quantify for accurate forecasting. The noise clustering approach is based on the concept of first defining a noise cluster and then defining a similarity or dissimilarity measure for the noise cluster.

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Author information

Authors and Affiliations

  1. Graduate School of Systems and Information Engineering, University of Tsukuba, Ibaraki, 305-8573, Japan

    Arnold C. Alanzado

  2. Department of Risk Engineering, School of Systems and Information Engineering, University of Tsukuba, Ibaraki, 305-8573, Japan

    Sadaaki Miyamoto

Authors
  1. Arnold C. Alanzado
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  2. Sadaaki Miyamoto
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Editor information

Editors and Affiliations

  1. IIIA, Artificial Intelligence Research Institute CSIC, Spanish National Research Council, Campus UAB s/n, 08193, Bellaterra, Catalonia, Spain

    Vicenç Torra

  2. Toho Gakuen, 3-1-10 Naka, Kunitachi, 186-0004, Tokyo, Japan

    Yasuo Narukawa

  3. Department of Risk Engineering, School of Systems and Information Engineering, University of Tsukuba, 305-8573, Ibaraki, Japan

    Sadaaki Miyamoto

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© 2005 Springer-Verlag Berlin Heidelberg

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Alanzado, A.C., Miyamoto, S. (2005). Fuzzy c-Means Clustering in the Presence of Noise Cluster for Time Series Analysis. In: Torra, V., Narukawa, Y., Miyamoto, S. (eds) Modeling Decisions for Artificial Intelligence. MDAI 2005. Lecture Notes in Computer Science(), vol 3558. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11526018_16

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  • DOI: https://doi.org/10.1007/11526018_16

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-27871-9

  • Online ISBN: 978-3-540-31883-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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