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International Visual Informatics Conference

IVIC 2015: Advances in Visual Informatics pp 38–50Cite as

Fuzzy-Based Shapelets for Mining Climate Change Time Series Patterns

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9429))

Abstract

It is difficult to identify visualized multi-climate change patterns from time series data due to the fact that the data begin to look similar over time. Traditionally, time series weather patterns are presented in the form of a linear graph, which is limited to discovering understandable climate change patterns. On the other hand, the Symbolic Aggregate Approximation (SAX) algorithm based on the Piecewise Aggregate Approximation (PAA), which is known as a popular method to solve this problem, has its limitations. Therefore, the aim of this research was to propose a fuzzy-based symbolic data representation, known as a Shapelet Patterns Algorithm (SPA), in order to come up with a Shapelet Pattern (SP) for climate change. The shapelet pattern was able to visualize climate change patterns in the form of coloured shapes to indicate annual changes in temperature patterns, such as cool, warm, hot and very hot. The experiment used the climate change data for 1985-2008 gathered from the Petaling Jaya station in the state of Selangor, Malaysia. The shapelet patterns revealed seven types of climate change patterns and presented detailed information on climate changes that can aid climate change experts in better decision making.

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Author information

Authors and Affiliations

  1. Data Mining and Optimization Group, Centre for Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia

    Ghassan Saleh Al-Dharhani, Zulaiha Ali Othman & Azuraliza Abu Bakar

  2. Institute of Climate Change, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia

    Sharifah Mastura Syed Abdullah

Authors
  1. Ghassan Saleh Al-Dharhani
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  2. Zulaiha Ali Othman
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  3. Azuraliza Abu Bakar
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  4. Sharifah Mastura Syed Abdullah
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Corresponding author

Correspondence to Ghassan Saleh Al-Dharhani .

Editor information

Editors and Affiliations

  1. Fac Info Science and Techn, Universiti Kebangsaan Malaysia, Selangor, Malaysia

    Halimah Badioze Zaman

  2. University of Cambridge, Cambridge, United Kingdom

    Peter Robinson

  3. Center for Digital Video Process, Dublin 9, Ireland

    Alan F. Smeaton

  4. Computer Science and Information Enginee, National Central University, Jhongli City, Taiwan

    Timothy K. Shih

  5. Kingston University, Kingston upon Thames, United Kingdom

    Sergio Velastin

  6. Universiti Kebangsaan Malaysia, Institute of Visual Informatics, Bangi, Malaysia

    Azizah Jaafar

  7. Universiti Kebangsaan Malaysia, Institute of Visual Informatics, Bangi, Malaysia

    Nazlena Mohamad Ali

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© 2015 Springer International Publishing Switzerland

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Al-Dharhani, G.S., Othman, Z.A., Bakar, A.A., Abdullah, S.M.S. (2015). Fuzzy-Based Shapelets for Mining Climate Change Time Series Patterns. In: Badioze Zaman, H., et al. Advances in Visual Informatics. IVIC 2015. Lecture Notes in Computer Science(), vol 9429. Springer, Cham. https://doi.org/10.1007/978-3-319-25939-0_4

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  • DOI: https://doi.org/10.1007/978-3-319-25939-0_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-25938-3

  • Online ISBN: 978-3-319-25939-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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