• Chinese Physics B
  • Vol. 29, Issue 8, (2020)
Jia Song, Tong-Feng Weng, Chang-Gui Gu, and Hui-Jie Yang
Author Affiliations
  • Business School, University of Shanghai for Science and Technology, Shanghai 200082, China
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    DOI: 10.1088/1674-1056/ab9287 Cite this Article
    Jia Song, Tong-Feng Weng, Chang-Gui Gu, Hui-Jie Yang. Patterns of cross-correlation in time series: A case study of gait trails[J]. Chinese Physics B, 2020, 29(8): Copy Citation Text show less
    Intrinsic modes in the records of normal stride intervals for the volunteer numbered 1. “Raw“, the normalized stride interval series. M1 to M17, the total of 16 intrinsic modes and the residual series obtained with the empirical mode decomposition algorithm (EMD).
    Fig. 1. Intrinsic modes in the records of normal stride intervals for the volunteer numbered 1. “Raw“, the normalized stride interval series. M1 to M17, the total of 16 intrinsic modes and the residual series obtained with the empirical mode decomposition algorithm (EMD).
    The statistical behaviors of the co-occurrences between the IMFs and residue. (a) The histogram of the co-occurrence distribution. (b) The log-log graph of the co-occurrence distribution. The vertical dotted line separates the co-occurrences into two sets, which obey power laws with significantly different scaling exponents.
    Fig. 2. The statistical behaviors of the co-occurrences between the IMFs and residue. (a) The histogram of the co-occurrence distribution. (b) The log-log graph of the co-occurrence distribution. The vertical dotted line separates the co-occurrences into two sets, which obey power laws with significantly different scaling exponents.
    The series of mode network (temporal network). A total of 25 mode networks out of the total of 30 ones are displayed. The other five mode networks are not shown, because there all the co-occurrences in them are less than the threshold θ = 0.23. The thickness of an edge is proportional to its weight and the size of a node is proportional to the summation of weights of its neighbors.
    Fig. 3. The series of mode network (temporal network). A total of 25 mode networks out of the total of 30 ones are displayed. The other five mode networks are not shown, because there all the co-occurrences in them are less than the threshold θ = 0.23. The thickness of an edge is proportional to its weight and the size of a node is proportional to the summation of weights of its neighbors.
    Evolution of the linkages. (a) Each pair of modes is labeled with a specific identification number from 1 to 70, as described in the text part. (b) The evolution of linkage’s weights is displayed with a heat map. The horizontal and vertical axes represent the mode network series C17×17j,j=1,2,…,30 and the 70 edges, respectively.
    Fig. 4. Evolution of the linkages. (a) Each pair of modes is labeled with a specific identification number from 1 to 70, as described in the text part. (b) The evolution of linkage’s weights is displayed with a heat map. The horizontal and vertical axes represent the mode network series C17×17j,j=1,2,,30 and the 70 edges, respectively.
    Evolutionary behavior of the mode network. (a) The global overlapping degree (summation of link’s weights that larger than 0.23) versus the identification number of mode network (time). (b) Average and error for every linkage that are shown in the heat-map in Fig. 4(b). The identification number is defined in Fig. 4(a).
    Fig. 5. Evolutionary behavior of the mode network. (a) The global overlapping degree (summation of link’s weights that larger than 0.23) versus the identification number of mode network (time). (b) Average and error for every linkage that are shown in the heat-map in Fig. 4(b). The identification number is defined in Fig. 4(a).
    Ego networks for the kernels. The nodes with red color are kernels. The other nodes with deep blue color are alters.
    Fig. 6. Ego networks for the kernels. The nodes with red color are kernels. The other nodes with deep blue color are alters.
    Jia Song, Tong-Feng Weng, Chang-Gui Gu, Hui-Jie Yang. Patterns of cross-correlation in time series: A case study of gait trails[J]. Chinese Physics B, 2020, 29(8):
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