The scaling of time series size towards detrended fluctuation analysis

Xiaolei Gao, Liwei Ren, Pengjian Shang () and Guochen Feng
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Xiaolei Gao: School of Science, Beijing Jiaotong University, Beijing 100044, P. R. China
Liwei Ren: School of Science, Beijing Jiaotong University, Beijing 100044, P. R. China
Pengjian Shang: School of Science, Beijing Jiaotong University, Beijing 100044, P. R. China
Guochen Feng: School of Science, Beijing Jiaotong University, Beijing 100044, P. R. China

International Journal of Modern Physics C (IJMPC), 2016, vol. 27, issue 11, 1-15

Abstract: In this paper, we introduce a modification of detrended fluctuation analysis (DFA), called multivariate DFA (MNDFA) method, based on the scaling of time series size N. In traditional DFA method, we obtained the influence of the sequence segmentation interval s, and it inspires us to propose a new model MNDFA to discuss the scaling of time series size towards DFA. The effectiveness of the procedure is verified by numerical experiments with both artificial and stock returns series. Results show that the proposed MNDFA method contains more significant information of series compared to traditional DFA method. The scaling of time series size has an influence on the auto-correlation (AC) in time series. For certain series, we obtain an exponential relationship, and also calculate the slope through the fitting function. Our analysis and finite-size effect test demonstrate that an appropriate choice of the time series size can avoid unnecessary influences, and also make the testing results more accurate.

Keywords: Multivariate detrended fluctuation analysis; scaling of time series size; artificial series; stock returns series; exponential relationship (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1142/S0129183116501382

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