Applied and Computational Mathematics
Volume 8, Issue 2, April 2019, Pages: 44-49
Received: Jul. 27, 2019;
Published: Jul. 29, 2019
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Lai Simin, School of Mathematics and Information Science, Guangzhou University, Guangzhou, China
Wan Li, School of Mathematics and Information Science, Guangzhou University, Guangzhou, China; Key Laboratory of Mathematics and Interdisciplinary Sciences of Guangdong Higher Education Institutes, Guangzhou University, Guangzhou, China
Zeng Xiangjian, School of Mathematics and Information Science, Guangzhou University, Guangzhou, China
Data missing often affects the characteristics of the sequence. Using appropriate methods to process the missing data is the premise and guarantee to obtain high quality information. In this study, a fractal interpolation method is proposed to fill the missing data with self-similar feature sequences. Two sets of binomial multifractal sequences with parameters of 0.25 and 0.35 are taken as the research objects, and the Hurst index value of the sequence after filling processing is calculated by MF-DMA, which verifies the practicability of the fractal interpolation filling method. At the same time, the method is applied to multi-fractal sequences with missing rates of 10%, 15% and 20% respectively, and compared with the filling effects of deletion method and random filling method, then, the applicability of the three methods is obtained. The results show that, for binomial multifractal sequences with different missing ratios, the Hurst index of the sequence processed by fractal interpolation has the highest degree of fitting with the theoretical value, its effect of repairing the fractal sequence is better than the other two methods, and has a good application prospect.
Comparative Analysis of Multi-fractal Data Missing Processing Methods, Applied and Computational Mathematics.
Vol. 8, No. 2,
2019, pp. 44-49.
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