Multiscale Fluctuation Features of the Dynamic Correlation between Bivariate Time Series

被引:1
|
作者
Jiang, Meihui [1 ,2 ,3 ]
Gao, Xiangyun [1 ,2 ,3 ]
An, Haizhong [1 ,2 ,3 ]
Jia, Xiaoliang [1 ,2 ,3 ]
Sun, Xiaoqi [1 ,2 ,3 ]
机构
[1] China Univ Geosci, Sch Humanities & Econ Management, Beijing 100083, Peoples R China
[2] Minist Land & Resources, Key Lab Carrying Capac Assessment Resource & Envi, Beijing 100083, Peoples R China
[3] Minist Land & Resources, Open Lab Talents Evaluat, Beijing 100083, Peoples R China
基金
中国国家自然科学基金;
关键词
COMPLEX NETWORK; WAVELET TRANSFORM; STOCK MARKETS; OIL PRICES; CONNECTIVITY; TRANSMISSION; PATTERNS;
D O I
10.1155/2016/4742060
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
The fluctuation of the dynamic correlation between bivariate time series has some special features on the time-frequency domain. In order to study these fluctuation features, this paper built the dynamic correlation network models using two kinds of time series as sample data. After studying the dynamic correlation networks at different time-scales, we found that the correlation between time series is a dynamic process. The correlation is strong and stable in the long term, but it is weak and unstable in the short and medium term. There are key correlation modes which can effectively indicate the trend of the correlation. The transmission characteristics of correlation modes show that it is easier to judge the trend of the fluctuation of the correlation between time series from the short term to long term. The evolution of media capability of the correlation modes shows that the transmission media in the long term have higher value to predict the trend of correlation. This work does not only propose a new perspective to analyze the correlation between time series but also provide important information for investors and decision makers.
引用
收藏
页数:9
相关论文
共 50 条
  • [1] Research on fluctuation of bivariate correlation of time series based on complex networks theory
    Gao Xiang-Yun
    An Hai-Zhong
    Fang Wei
    [J]. ACTA PHYSICA SINICA, 2012, 61 (09)
  • [2] Multiscale analysis of the intensity fluctuation in a time series of dynamic speckle patterns
    Federico, Alejandro
    Kaufmann, Guillermo H.
    [J]. APPLIED OPTICS, 2007, 46 (11) : 1979 - 1985
  • [3] A new tendency correlation coefficient for bivariate time series
    Jian Zhou
    Zhongsheng Hua
    [J]. Rendiconti Lincei. Scienze Fisiche e Naturali, 2021, 32 : 479 - 491
  • [4] A new tendency correlation coefficient for bivariate time series
    Zhou, Jian
    Hua, Zhongsheng
    [J]. RENDICONTI LINCEI-SCIENZE FISICHE E NATURALI, 2021, 32 (03) : 479 - 491
  • [5] An alternative measure of positive correlation for bivariate time series
    Bapat, Sudeep R.
    [J]. COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, 2022, 51 (06) : 3252 - 3258
  • [6] Multiscale multifractal detrended fluctuation analysis of multivariate time series
    Fan, Qingju
    Liu, Shuanggui
    Wang, Kehao
    [J]. PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, 2019, 532
  • [7] Multiresolution transmission of the correlation modes between bivariate time series based on complex network theory
    Huang, Xuan
    An, Haizhong
    Gao, Xiangyun
    Hao, Xiaoqing
    Liu, Pengpeng
    [J]. PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, 2015, 428 : 493 - 506
  • [8] A new correlation for bivariate time series with a higher order of integration
    Bapat, Sudeep R.
    [J]. COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, 2020, 49 (10) : 2546 - 2558
  • [9] A new correlation coefficient for bivariate time-series data
    Erdem, Orhan
    Ceyhan, Elvan
    Varli, Yusuf
    [J]. PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, 2014, 414 : 274 - 284
  • [10] Research on Correlation Analysis Method of Time Series Features Based on Dynamic Time Warping Algorithm
    Liu, Yiming
    Guo, Huadong
    Zhang, Lu
    Liang, Dong
    Zhu, Qi
    Liu, Xuting
    Lv, Zhuoran
    Dou, Xinyu
    Gou, Yiting
    [J]. IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2023, 20