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Multifractal detrended fluctuation analysis based on fractal fitting: The long-range correlation detection method for highway volume data

Author

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  • Dai, Meifeng
  • Hou, Jie
  • Ye, Dandan

Abstract

In this paper, we investigate the traffic time series for volume data observed on the Guangshen highway. We introduce a multifractal detrended fluctuation analysis based on fractal fitting (MFDFA-FF), which is one of the most effective methods to detect long-range correlations of time series. Through effective detecting of long-range correlations, highway volume can be predicted more accurately. In order to get a better detrend effect, we use fractal fitting to replace polynomial fitting in detrend process, the result shows that fractal fitting can get a better detrend effect than polynomial fitting and the MFDFA-FF method can achieve a more accurate research result. Then we introduce the Legendre spectrum to detect the multifractal property characterized by the long-range correlation and multifractality of Guangshen highway volume data.

Suggested Citation

  • Dai, Meifeng & Hou, Jie & Ye, Dandan, 2016. "Multifractal detrended fluctuation analysis based on fractal fitting: The long-range correlation detection method for highway volume data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 444(C), pages 722-731.
    • Handle: RePEc:eee:phsmap:v:444:y:2016:i:c:p:722-731
    DOI: 10.1016/j.physa.2015.10.073
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    Cited by:

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    4. Saâdaoui, Foued, 2018. "Testing for multifractality of Islamic stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 496(C), pages 263-273.

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