A local fitting based multifractal detrend fluctuation analysis method

Wang, Jian; Huang, Menghao; Wu, Xinpei; Kim, Junseok

doi:10.1016/j.physa.2023.128476

Cited by 2 publications

(1 citation statement)

References 37 publications

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“…Yang et al [42] addressed the potential presence of negative values in the original MF-DFA model by introducing sign retention to enhance performance, resulting in the sign retention model MF-S-DFA. Additionally, Wang et al [43] proposed the MF-LF-DFA algorithm, which improves the performance of MF-DFA by reasonably setting the fitting order for different local intervals based on the fluctuation characteristics of the sequence. We calculate the Hurst values corresponding to different ranges of s. Let H3 err (q) and H4 err (q) denote the absolute differences between the results computed using MF-S-DFA and LF-MF-DFA methods and the analytical values, respectively.…”

Section: Multifractal Analysis With Differentsmentioning

confidence: 99%

Modified MF-DFA Model Based on LSSVM Fitting

Wang,

Zhong,

Yue

et al. 2024

Fractal Fract

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This paper proposes a multifractal least squares support vector machine detrended fluctuation analysis (MF-LSSVM-DFA) model. The system is an extension of the traditional MF-DFA model. To address potential overfitting or underfitting caused by the fixed-order polynomial fitting in MF-DFA, LSSVM is employed as a superior alternative for fitting. This approach enhances model accuracy and adaptability, ensuring more reliable analysis results. We utilize the p model to construct a multiplicative cascade time series to evaluate the performance of MF-LSSVM-DFA, MF-DFA, and two other models that improve upon MF-DFA from recent studies. The results demonstrate that our proposed modified model yields generalized Hurst exponents h(q) and scaling exponents τ(q) that align more closely with the analytical solutions, indicating superior correction effectiveness. In addition, we explore the sensitivity of MF-LSSVM-DFA to the overlapping window size s. We find that the sensitivity of our proposed model is less than that of MF-DFA. We find that when s exceeds the limited range of the traditional MF-DFA, h(q) and τ(q) are closer than those obtained in MF-DFA when s is in a limited range. Meanwhile, we analyze the performances of the fitting of the two models and the results imply that MF-LSSVM-DFA achieves a better outstanding performance. In addition, we put the proposed MF-LSSVM-DFA into practice for applications in the medical field, and we found that MF-LSSVM-DFA improves the accuracy of ECG signal classification and the stability and robustness of the algorithm compared with MF-DFA. Finally, numerous image segmentation experiments are adopted to verify the effectiveness and robustness of our proposed method.

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Section: Multifractal Analysis With Differentsmentioning

confidence: 99%

Modified MF-DFA Model Based on LSSVM Fitting

Wang,

Zhong,

Yue

et al. 2024

Fractal Fract

Self Cite

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Relaxing Daily Price Limits and Stock Market Cross-Correlation: Evidence from MF-X-DMA Analysis

Ruan¹,

Hu²,

Zhang³

et al. 2023

Fluct. Noise Lett.

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The daily price limits in the ChiNext stock market were relaxed from [Formula: see text]% to [Formula: see text]% on 24 August 2020. Using the multifractal detrended moving average cross-correlation analysis (MF-X-DMA) method, we find that relaxing daily price limits leads to a greater degree of multifractality of the ChiNext stock market, suggesting that the relaxation of daily price limits harms stock market efficiency. In addition, the positive cross-correlation between ChiNext and Chinese main board stock markets becomes weaker, i.e., relaxing daily price limits also decreases the connection between ChiNext and other stock markets. Moreover, there is an increase in the degree of the cross-correlation multifractality between ChiNext and Chinese main board stock markets, suggesting that the linkage of ChiNext and other stock markets is more complicated and risky after the relaxation of daily price limits. Our findings fulfill related literature from the perspective of multifractality and have important implications for investors.

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A local fitting based multifractal detrend fluctuation analysis method

Cited by 2 publications

References 37 publications

Modified MF-DFA Model Based on LSSVM Fitting

Modified MF-DFA Model Based on LSSVM Fitting

Relaxing Daily Price Limits and Stock Market Cross-Correlation: Evidence from MF-X-DMA Analysis

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