Exploring the temporal structure of time series data for hazardous liquid pipeline incidents based on complex network theory

2019 ◽  
Vol 26 ◽  
pp. 100308
Author(s):  
Liu Shengli ◽  
Liang Yongtu
Keyword(s):  
Time Series ◽  
Complex Network ◽  
Network Theory ◽  
Time Series Data ◽  
Temporal Structure ◽  
Series Data ◽  
Complex Network Theory

Characterizing time series of near-miss accidents in metro construction via complex network theory

2017 ◽  
Vol 98 ◽  
pp. 145-158 ◽  
Author(s):  
Cheng Zhou ◽  
Lieyun Ding ◽  
Miroslaw J. Skibniewski ◽  
Hanbin Luo ◽  
Shuangnan Jiang
Keyword(s):  
Time Series ◽  
Complex Network ◽  
Network Theory ◽  
Near Miss ◽  
Complex Network Theory

scholarly journals Using causality and correlation analysis to decipher microbial interactions in activated sludge

2021 ◽  
Author(s):  
Weiwei Cai ◽  
Xiangyu Han ◽  
Hong Yao
Keyword(s):  
Time Series ◽  
Activated Sludge ◽  
Network Theory ◽  
Time Series Data ◽  
Microbial Interactions ◽  
Series Data ◽  
Natural Environments ◽  
Cell Aggregate ◽  
Causality Analysis ◽  
Mutualistic Relationship

Network theory is widely used to understand microbial interactions in activated sludge and numerous other artificial and natural environments. However, when using correlation-based methods, it is not possible to identify the directionality of interactions within microbiota. Based on the classic Granger test of sequencing-based time-series data, a new Microbial Causal Correlation Network (MCCN) was constructed with distributed ecological interaction on the directed, associated links. As a result of applying MCCN to a time series of activated sludge data, we found that the hub species OTU56, classified as belonging the genus Nitrospira, was responsible for completing nitrification in activated sludge, and mainly interacted with Proteobacteria and Bacteroidetes in the form of amensal and commensal relationships, respectively. Phylogenetic tree suggested a mutualistic relationship between Nitrospira and denitrifiers. Zoogloea displayed the highest ncf value within the classified OTUs of the MCCN, indicating that it could be a foundation for activated sludge through forming the characteristic cell aggregate matrices into which other organisms embed during floc formation. Overall, the introduction of causality analysis greatly expands the ability of a network to shed a light on understanding the interactions between members of a microbial community.

scholarly journals Let's Do the Time Warp Again: Non-linear time series matching as a tool for sequentially structured data in ecology

2021 ◽  
Author(s):  
Jens C Hegg ◽  
Brian P Kennedy
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Linear Time ◽  
Temporal Structure ◽  
Structured Data ◽  
Series Data ◽  
Small Scale ◽  
Temporal Data ◽  
Ecological Data ◽  
Aggregation Techniques

Ecological patterns are often fundamentally chronological. However, generalization of data is necessarily accompanied by a loss of detail or resolution. Temporal data in particular contains information not only in data values but in the temporal structure, which is lost when these values are aggregated to provide point estimates. Dynamic Time Warping (DTW) is a time series comparison method that is capable of efficiently comparing series despite temporal offsets that confound other methods. The DTW method is both efficient and remarkably flexible, capable of efficiently matching not only time series but any sequentially structured dataset, which has made it a popular technique in machine learning, artificial intelligence, and big data analytical tasks. DTW is rarely used in ecology despite the ubiquity of temporally structured data. As technological advances have increased the richness of small-scale ecological data, DTW may be an attractive analysis technique because it is able to utilize the additional information contained in the temporal structure of many ecological datasets. In this study we use an example dataset of high-resolution fish movement records obtained from otolith microchemistry to compare traditional analysis techniques with DTW clustering. Our results suggest that DTW is capable of detecting subtle behavioral patterns within otolith datasets which traditional data aggregation techniques cannot. These results provide evidence that the DTW method may be useful across many of the temporal data types commonly collected in ecology, as well other sequentially ordered "pseudo time series" data such as classification of species by shape.

scholarly journals Putting the “dynamic” back into dynamic functional connectivity

2017 ◽  
Author(s):  
Stewart Heitmann ◽  
Michael Breakspear
Keyword(s):  
Time Series ◽  
Functional Connectivity ◽  
Large Scale ◽  
Time Series Data ◽  
Temporal Structure ◽  
Series Data ◽  
Dynamic Functional Connectivity ◽  
Time Resolved ◽  
Phase Amplitude ◽  
The Brain

AbstractThe study of fluctuations in time-resolved functional connectivity is a topic of substantial current interest. As the term “dynamic functional connectivity” implies, such fluctuations are believed to arise from dynamics in the neuronal systems generating these signals. While considerable activity currently attends to methodological and statistical issues regarding dynamic functional connectivity, less attention has been paid toward its candidate causes. Here, we review candidate scenarios for dynamic (functional) connectivity that arise in dynamical systems with two or more subsystems; generalized synchronization, itinerancy (a form of metastability), and multistability. Each of these scenarios arise under different configurations of local dynamics and inter-system coupling: We show how they generate time series data with nonlinear and/or non-stationary multivariate statistics. The key issue is that time series generated by coupled nonlinear systems contain a richer temporal structure than matched multivariate (linear) stochastic processes. In turn, this temporal structure yields many of the phenomena proposed as important to large-scale communication and computation in the brain, such as phase-amplitude coupling, complexity and flexibility. The code for simulating these dynamics is available in a freeware software platform, the “Brain Dynamics Toolbox”.

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