論文

査読有り 本文へのリンクあり
2019年9月

Generalized theory for detrending moving-average cross-correlation analysis: A practical guide

Chaos, Solitons and Fractals: X
  • Akio Nakata
  • ,
  • Miki Kaneko
  • ,
  • Taiki Shigematsu
  • ,
  • Satoshi Nakae
  • ,
  • Naoko Evans
  • ,
  • Chinami Taki
  • ,
  • Tetsuya Kimura
  • ,
  • Ken Kiyono

3
記述言語
掲載種別
研究論文(学術雑誌)
DOI
10.1016/j.csfx.2020.100022

© 2020 The Author(s) To evaluate the long-range cross-correlation in non-stationary bi-variate time-series, detrending-operation-based analysis methods such as the detrending moving-average cross-correlation analysis (DMCA), are widely used. However, its mathematical foundation has not been well established. In this paper, we propose a generalized theory to form the foundation of DMCA-type methods and introduce the higher-order DMCA in which Savitzky-Golay filters are employed as the detrending operator. Using this theory, we can understand the mathematical basis of DMCA-type methods. Our theory establishes a rigorous relationship between the DMCA-type analysis, the cross-correlation function analysis, and the cross-power spectral analysis. Based on the mathematical validity, we provide a practical guide for the use of higher-order DMCA. Additionally, we present illustrative results of a numerical and real-world analysis. To achieve reliable and accurate detection of the long-range cross-correlation, we emphasize the importance of time-lag estimation and time scale correction in DMCA, which has not been pointed out in the previous studies.

リンク情報
DOI
https://doi.org/10.1016/j.csfx.2020.100022
Scopus
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85081051176&origin=inward 本文へのリンクあり
Scopus Citedby
https://www.scopus.com/inward/citedby.uri?partnerID=HzOxMe3b&scp=85081051176&origin=inward
ID情報
  • DOI : 10.1016/j.csfx.2020.100022
  • eISSN : 2590-0544
  • SCOPUS ID : 85081051176

エクスポート
BibTeX RIS