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Multifractal theory with its applications in data management

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  • Yuxin Zhao
  • Shuai Chang
  • Chang Liu

Abstract

The extraction of interesting information from enormous and irregular datasets has always been a significant research topic. For the datasets with irregular distribution and self-similarity, multifractal theory is the most appreciated approach and has been successfully applied in many fields, such as financial analysis, image processing, medical diagnosis, earthquake study, etc. In this paper, we make a detailed analysis and summary on three main functions, namely multifractal structure diagnosis, tendency and singularity analysis. Finally, some experiments based on oil prices data and spatial physical data are carried out to validate its performance effectively. Copyright Springer Science+Business Media New York 2015

Suggested Citation

  • Yuxin Zhao & Shuai Chang & Chang Liu, 2015. "Multifractal theory with its applications in data management," Annals of Operations Research, Springer, vol. 234(1), pages 133-150, November.
    • Handle: RePEc:spr:annopr:v:234:y:2015:i:1:p:133-150:10.1007/s10479-014-1599-1
    DOI: 10.1007/s10479-014-1599-1
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    1. Rosella Castellano & Roy Cerqueti & Giulia Rotundo, 2020. "Exploring the financial risk of a temperature index: a fractional integrated approach," Annals of Operations Research, Springer, vol. 284(1), pages 225-242, January.
    2. Roy Cerqueti & Viviana Fanelli, 2021. "Long memory and crude oil’s price predictability," Annals of Operations Research, Springer, vol. 299(1), pages 895-906, April.

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