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REFII Model as a Base for Data Mining Techniques Hybridization with Purpose of Time Series Pattern Recognition

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Hybrid Soft Computing Approaches

Part of the book series: Studies in Computational Intelligence ((SCI,volume 611))

Abstract

The article will present the methodology for holistic time series analysis, based on time series transformation model REFII (REFII is an acronym for Raise-Equal-Fall and the model version is II or 2) Patel et al. (Mining motifs in massive time series databases, 2002) [1], Perng and Parker (SQL/LPP: a time series extension of SQL based on limited patience patterns, 1999) [2], Popivanov and Miller (Similarity search over time series data using wavelets, 2002) [3]. The main purpose of REFII model is to automate time series analysis through a unique transformation model of time series. The advantage of this approach to a time series analysis is the linkage of different methods for time series analysis linking traditional data mining tools in time series, and constructing new algorithms for analyzing time series. REFII model is not a closed system, which means that there is a finite set of methods. This is primarily a model used for transformation of values of time series, which prepares data used by different sets of methods based on the same model of transformation in the domain of problem space. REFII model gives a new approach in time series analysis based on a unique model of transformation, which is a base for all kind of time series analyses. In combination with elements of other methods, such as self-organizing maps or frequent-pattern trees, REFII models can make new hybrid methods for efficient time temporal data mining. Similar principle of hybridization could be used as a tool for time series temporal pattern recognition. The article describes real case study illustrating practical application of described methodology.

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Author information

Authors and Affiliations

  1. Raiffeisenbank Austria, Zagreb, Croatia

    Goran Klepac

  2. IN2data Data Science Company, Zagreb, Croatia

    Robert Kopal & Leo Mršić

Authors
  1. Goran Klepac
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  2. Robert Kopal
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  3. Leo Mršić
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Corresponding author

Correspondence to Goran Klepac .

Editor information

Editors and Affiliations

  1. Department of Information Technology, RCC Institute of Information Technology, Kolkata, West Bengal, India

    Siddhartha Bhattacharyya

  2. Department of Computer and System Sciences, Visva-Bharati University, Santiniketan, West Bengal, India

    Paramartha Dutta

  3. Department of Computer Science and Technology, Indian Institute of Engineering Science and Technology, Howrah, India

    Susanta Chakraborty

Appendix: Module for Pattern Recognition in Time Series

Appendix: Module for Pattern Recognition in Time Series

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Klepac, G., Kopal, R., Mršić, L. (2016). REFII Model as a Base for Data Mining Techniques Hybridization with Purpose of Time Series Pattern Recognition. In: Bhattacharyya, S., Dutta, P., Chakraborty, S. (eds) Hybrid Soft Computing Approaches. Studies in Computational Intelligence, vol 611. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2544-7_8

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  • DOI: https://doi.org/10.1007/978-81-322-2544-7_8

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