Bayesian detection of event spreading pattern from multivariate binary time series

2021 ◽  
pp. 1-38
Author(s):  
Jie Hu ◽  
Zirui Chen ◽  
Wenwei Lin ◽  
Xiaodan Fan
Keyword(s):  
Time Series ◽  
Spreading Pattern ◽  
Binary Time Series

Time is Not A Theoretical Variable

2010 ◽  
Vol 18 (3) ◽  
pp. 293-294 ◽  
Author(s):  
Nathaniel Beck
Keyword(s):  
Time Series ◽  
Cross Section ◽  
Duration Dependence ◽  
Cross Section Data ◽  
Section Data ◽  
Data Analyst ◽  
Binary Time Series ◽  
Simple Cubic ◽  
Cubic Polynomial

Carter and Signorino (2010) (hereinafter “CS”) add another arrow, a simple cubic polynomial in time, to the quiver of the binary time series—cross-section data analyst; it is always good to have more arrows in one's quiver. Since comments are meant to be brief, I will discuss here only two important issues where I disagree: are cubic duration polynomials the best way to model duration dependence and whether we can substantively interpret duration dependence.

Binary time series models driven by a latent process

2017 ◽  
Vol 2 ◽  
pp. 117-130 ◽  
Author(s):  
Konstantinos Fokianos ◽  
Theodoros Moysiadis
Keyword(s):  
Time Series ◽  
Time Series Models ◽  
Binary Time Series ◽  
Latent Process

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.

Sign in / Sign up

Export Citation Format

Share Document