A novel time-frequency multilayer network for multivariate time series analysis

Abstract

Unveiling complex dynamics of natural systems from a multivariate time series represents a research hotspot in a broad variety of areas. We develop a novel multilayer network analysis framework, i.e. multivariate time-frequency multilayer network (MTFM network), to peer into the complex system dynamics. Through mapping the system features into different frequency-based layers and inferring interactions (edges) among different channels (nodes), the MTFM network allows efficiently integrating time, frequency and spatial information hidden in a multivariate time series. We employ two dynamic systems to illustrate the effectiveness of the MTFM network. We first apply the MTFM network to analyze the 48-channel measurements from industrial oil-water flows and reveal the complex dynamics ruling the transition of different flow patterns. The MTFM network is then utilized to analyze 30-channel fatigue driving electroencephalogram signals. The results demonstrate that MTFM network enables to quantitatively characterize brain behavior associated with fatigue driving. Our MTFM network enriches the multivariate time series analysis theory and helps to better understand the complicated dynamical behaviors underlying complex systems.

Synchronization to extreme events in moving agents

“…Here we note that the obtained results remain valid for any other value of the network size N. Particularly, higher N would need lower values of the interaction strength K to achieve synchronization for fixed values of other network parameters (results not shown here). Even that inverse proportional relation between N and K is obvious from equation (8). Since, interaction zones are fixed in plane, mobility of nodes have huge significance in obtaining complete synchronization.…”

“…Since, interaction zones are fixed in plane, mobility of nodes have huge significance in obtaining complete synchronization. On the other hand, the modulus of velocity v does not appear explicitly in the relation (8). So, in order to understand the simultaneous influence of K and v on synchrony, we plot the phase diagram in the K-v parameter space, in figure 3.…”

“…">IntroductionResearch to understand the interplay between complex networks and the dynamical properties of coupled oscillators has been a hotspot for the last few decades and the developing phenomenon of synchronization [1-4] is one of the most important dynamical processes that has been in the center of these researches. Cooperation [5,6] and time series analysis [7,8] in complex network have been studied in the past few years. From the perspective of synchronization among coupled oscillators placed into a complex network [9,10], the correlation between the network's topology and local dynamics is quite decisive.…”

Bounds on Nirmala energy of graphs

“…Inspired by this study, many graph energy variants are defined and extensive studies are carried out on energy bounds (see [4], [16], [23]). Various applications of graph energy can be found in network analysis [21], computer science [3] and process analysis [10]. The Nirmala matrix…”

Multilayer brain network combined with deep convolutional neural network for detecting major depressive disorder

“…Moreover, complex system exhibits obvious multiple characteristics, so does the brain. As the latest development in complex networks, multilayer network [31][32][33][34] possesses large number of nodes in multiple layers with different types of edges. Different layers correspond to different aspects of the studied system and allow providing a more intuitive and accurate characterization.…”