A three-level model for binary time-series data: the effects of air pollution on school absences in the Southern California Children's Health Study

2005 ◽  
Vol 24 (7) ◽  
pp. 1103-1115 ◽  
Author(s):  
Virginie Rondeau ◽  
Kiros Berhane ◽  
Duncan C. Thomas
Keyword(s):  
Air Pollution ◽  
Time Series ◽  
Southern California ◽  
Time Series Data ◽  
Children's Health ◽  
Health Study ◽  
Series Data ◽  
Binary Time Series ◽  
School Absences ◽  
Level Model

scholarly journals Full reconstruction of simplicial complexes from binary time-series data

2021 ◽  
Author(s):  
Huan Wang ◽  
Chuang Ma ◽  
Han-Shuang Chen ◽  
Ying-Cheng Lai ◽  
Hai-Feng Zhang
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Simplicial Complexes ◽  
Series Data ◽  
Many Body ◽  
Stochastic Disturbance ◽  
Binary Time Series ◽  
Full Reconstruction ◽  
Three Body ◽  
Many Body Interactions

Abstract Previous efforts on data-based reconstruction focused on complex networks with pairwise or two-body interactions. There is a growing interest in networks with high-order or many-body interactions, raising the need to reconstruct such networks based on observational data. We develop a general framework combining statistical inference and expectation maximization to fully reconstruct 2-simplicial complexes with two- and three-body interactions based on binary time-series data from social contagion dynamics. We further articulate a two-step scheme to improve the reconstruction accuracy while significantly reducing the computational load. Through synthetic and real-world 2-simplicial complexes, we validate the framework by showing that all the connections can be faithfully identified and the full topology of the 2-simplicial complexes can be inferred. The effects of noisy data or stochastic disturbance are studied, demonstrating the robustness of the proposed framework.

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