COMPLEX NETWORK FROM PSEUDOPERIODIC TIME SERIES TOPOLOGY VERSUS DYNAMICS

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The transmission among all modes in the cluster will not last more than days at different time scales. Table 1 The top 5 modes with weighted out-degrees in different periods. Nonlinear Phenomena, , , 7— See all formats and pricing Online. However, we should consider the weighted outdegree of the nodes in other clusters to measure the media capability between clusters. The coarse graining method processes all the elements in matrix R according to Eq. Volume 10 Issue 6 Dec , pp. A series of degree distribution curves are nected graph, where each pair of nodes are assigned a obtained by using different threshold!

Because the periodic exponent can be determined [8]. The reason is that all of the trajectories of the nodes in community 2, 3 and 4 move between the two attractors. Showing of 11 references. Investigating the evolution dynamics and the relationships among variables is a significant way to understand the variations and evolution mechanisms of the relationships among variables. For example, in Figure 7 , nodes with different colors represent different energy market statuses. The dynamic states of the time series on different time scales can be observed by selecting different values for n.

Volume 15 Issue 1 Janpp.

Neteork work is the primary motivation for much of the current work on mapping time series to the complex networks. The interrelationships among the closing prices of the three types of energy are significant in the longterm and delayed in the short term. In some cases, a small color block is inserted into a larger block. The main advantage of the overlapping sliding window approach is that each data segment includes some information from the previous data segment.

Let r represents an timee in the matrix Re. Figure 5 The cumulative percentage distribution over four periods. Specifically, the trajectory the distance as Dij! This is Tsimring, Rev. As this distinction to the unstable periodic orbits that form the expected, dynamic VND usually assumes a much higher value skeleton of the chaotic attractor.

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Lastly, complex networks are constructed at the threshold values within the selected interval that can better describe the dynamic states of the time series. Most economic time series are nonlinear and unsteady. A complex network is then constructed based on this analysis. My Content 1 Recently viewed eynamics Reconstructing time se As noted above, one cluster will transform into other clusters.

Abstract In this paper, a new approach to map time series into complex networks based on the cross correlation interval is proposed for the analysis of dynamic states of time series on different scales. Numerous scholars have made significant topoolgy to this field.

Complex network from pseudoperiodic time series: topology versus dynamics.

The basic statistical prop- for coronary care patients than for volunteers. Major modes c be identified via the weighted outdegree of nodes, and we defined w i dynamixs u t as follows:. After implementing a coarse graining method, the correlation coefficients of energy prices in each segment were defined as modes. The states are two extreme situations. References Publications referenced by this paper. Without those correlation modes working as fersus, the network may separate into different clusters.

Next, we study the evolution dynamics of the energy price time series by analyzing the structures of the complex networks in different periods. By using the comment function pseudoperiodicc degruyter. The efficiency E of the complex network is defined as: Each segment contains a corresponding number of values 50,and Volume 4 Issue 4 Decpp.

We only consider the correlation among energy prices, and there is not direction of causal relationship for energy prices. It is observed from Figure 5 that the nodes of community 2, 3 and 4 are closely connected. Volume 2 Issue 4 Decpp. Our previous studies focused on the autocorrelation and autoregression of energy prices [ 2829 ] and the correlation and regression between two energy prices [ 3031 ] by reconstructing networks based on energy price time series.

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Section 2 describes the proposed approach in detail. Volume 10 Issue 6 Decpp. These statistical properties actually reflect experimental time series. Thus, first, we divide natural gas, coal and crude oil price time series into a series of segments via a set of temporal sliding windows and then calculate the correlation coefficients for each pair of energy prices in each segment.

We can see that the newtork degree distribution for weight between each pair of nodes as the distance between a coronary care unit patient demonstrates more prominent corresponding cycles in phase space. The transmission pattern explains the relationships among modes.

If more variables are present, the correlation mode will be long and complex. Zhang, three to four UPOs, while various pathological conditions X. Based on this work, the dynamics characteristics of sea clutter can be explored more deeply. A series of degree distribution curves are nected graph, where each pair of nodes are assigned a obtained by using different threshold!

Complex network from pseudoperiodic time series: topology versus dynamics.

Large nodes have higher topologg outdegrees than small nodes. We list the top five correlation modes ranked by the weighted outdegree for four different periods associated with four temporal sliding window sizes details in the supplementary Table S2. Figure 11 Transmission possibilities between clusters at different time periods. Volume 5 Issue 4 Decpp. The authors in differentiating healthy from pathological groups.