Joint modeling of local and global temporal dynamics for multivariate time series forecasting with missing values Proceedings of the AAAI Conference on Artificial Intelligence, 2020. 论文链接: Joint Modeling of Local and Global Temporal Dynamics for Multivariate Time Series Forecasting with Missing Values...
Cao Liangyue,Mees A,Judd K.Dynamics from multivariate time series.Physica D Nonlinear Phenomena. 1998L. Cao, A. Mees, and K. Judd, "Dynamics from multivariate time series," Physica D: Nonlinear Phenomena, vol. 121, no. 1, pp. 75-88, 1998....
TheInformationDynamicsToolkitxl(IDTxl) is a comprehensive software package for efficient inference of networks and their node dynamics from multivariate time series data using information theory. IDTxl provides functionality to estimate the following measures: ...
22 Optimal transport in statistics and Pitman efficient multivariate distribution-f 44:27 Localized Patterns in Population Models with the Large Biased Movement and Stron 55:05 Infinite Systems of Linear Equations 49:58 Fluctuations in the distribution of Frobenius automorphisms in number field exte 56...
Observability of multivariate differential embeddings. J. Phys. A: Mathematical and General 38, 6311–6326 (2005). Article MathSciNet MATH Google Scholar Aguirre, L. A., Portes, L. L. & Letellier, C. Structural, dynamical and symbolic observability: From dynamical systems to networks. Plos...
For example, as a disease progresses, the brain dynamics may be gradually altered to transit from one phase to another, or to approach or repel from a phase transition curve. In fact, the method is applicable to general multivariate time series. Deployment of the present method to other ...
Long memory dynamics for multivariate dependence under heavy tailsdoi:10.1016/j.jempfin.2014.09.007FractionalintegrationCorrelationStudent'stcopulaTime-varyingdependenceMultivariatevolatilityWe develop a new simultaneous time series model for volatility and dependence in daily financial return series that are ...
Several statistical methods, such as correlation analysis, principal component analysis, cluster analysis, and multivariate analysis, have been used to detect wetland change factors (Hu et al., 2020; Jing et al., 2020; Li et al., 2021). Show abstract Quantitative estimation of the factors ...
In reality, the GNN computes its outcome from the complete multivariate state of the neighbors of a node. The interacting contagion and the metapopulation dynamics, unlike the simple and complex contagions, are examples of such multivariate cases. Their outcome is thus harder to visualize in a ...
To that end, we draw the payoff matrix Π from a multivariate normal distribution with $$\langle {{\rm{\Pi }}}_{{i}_{\mu },{i}_{\mu +1},\ldots ,{i}_{\mu -1}}^{\mu }{{\rm{\Pi }}}_{{i}_{\nu },{i}_{\nu +1},\ldots ,{i}_{\nu -1}}^{\nu }\rangle ...