Addressing Information Asymmetry: Deep Temporal Causality Discovery for Mixed Time Series [0.03%]
解决信息不对称问题：混合时间序列的深度时序因果关系发现
Jiawei Chen,Chunhui Zhao
Jiawei Chen
While existing causal discovery methods mostly focus on continuous time series, causal discovery for mixed time series encompassing both continuous variables (CVs) and discrete variables (DVs) is a fundamental yet underexplored problem....Together with nonlinearity and high dimensionality, mixed time series pose significant challenges for causal discovery....Thereupon, we propose a generic deep mixed time series temporal causal discovery framework. Our key idea is to adaptively recover LCVs from DVs with the guidance of CVs and perform causal discovery in a unified continuous-valued space.
Mixed time series approaches for forecasting the daily number of hospital blood collections [0.03%]
混合时间序列方法预测医院每日血液采集数量
Xinli Zhang,Xin Zhao,Xiaoying Mou et al.
Xinli Zhang et al.
Finally, we built a combined prediction model considering mixed time series to predict the number of blood collections in the hospital....Conclusion: The combined prediction model of mixed time series can reflect the change in the blood collections number due to the influence of internal and external factors and can realize the blood collection prediction with a higher accuracy providing a new method for the prediction
Data on copula modeling of mixed discrete and continuous neural time series [0.03%]
关于混合离散连续神经时间序列的Copula建模数据研究
Meng Hu,Mingyao Li,Wu Li et al.
Meng Hu et al.
Recent work on copula has been expanded to jointly model mixed time series in neuroscience ("Hu et al., 2016, Joint Analysis of Spikes and Local Field Potentials using Copula" [1]). Here we present further data for joint analysis of spike and local field potential (LFP) with copula modeling.
A framework for the analysis of mixed time series/point process data--theory and application to the study of physiological tremor, single motor unit discharges and electromyograms [0.03%]
混合时间序列/点过程数据的分析框架—理论与生理震颤、单个运动单位放电和肌电图的研究
D M Halliday,J R Rosenberg,A M Amjad et al.
D M Halliday et al.