Stationarity of Manifold Time Series

Dehan Kong, Junhao Zhu, Zhaolei Zhang et al.

A Physics-Informed Spatiotemporal Deep Learning Framework for Turbulent Systems

Luca Menicali, Andrew Grace, David H. Richter et al.

A Statistician’s Overview of Physics-Informed Neural Networks for Spatio-Temporal Data

Christopher K. Wikle, Joshua North, Giri Gopalan et al.

A factor-copula latent-vine time series model for extreme flood insurance losses

Xiaoting Li, Harry Joe, Christian Genest

Structural Identification for Spatio-Temporal Dynamic Models

Cong Cheng, Yuan Ke, Wenyang Zhang et al.

Efficient Online Prediction for High-Dimensional Time Series via Joint Tensor Tucker Decomposition

Defeng Sun, Zhenting Luan, Haoning Wang et al.

Real-time prediction plays a vital role in various control systems, such as traffic congestion control and wireless channel resource allocation. In th...

Tail-robust factor modelling of vector and tensor time series in high dimensions

Haeran Cho, Matteo Barigozzi, Hyeyoung Maeng

Summary We study the problem of factor modelling vector- and tensor-valued time series in the presence of heavy tails in the data, w...

Identification and estimation for matrix time series CP-factor models

Qiwei Yao, Jinyuan Chang, Yue Du et al.

Spatiotemporal Besov Priors for Bayesian Inverse Problems

Shiwei Lan, Mirjeta Pasha, Shuyi Li et al.

Spectral change point estimation for high-dimensional time series by sparse tensor decompositionGet access

Xinyu ZhangandKung-Sik Chan

Estimation of Grouped Time-Varying Network Vector Autoregressive Models

Degui Li, Bin Peng, Songqiao Tang et al.

On Non-asymptotic Theory of Recurrent Neural Networks in Temporal Point Processes

Zhiheng Chen, Guanhua Fang, Wen Yu

Temporal point process (TPP) is an important tool for modeling and predicting irregularly timed events across various domains. Recently, the recurrent...

Dynamic Bayesian Learning for Spatiotemporal Mechanistic Models

Sudipto Banerjee, Xiang Chen, Ian Frankenburg et al.

We develop an approach for Bayesian learning of spatiotemporal dynamical mechanistic models. Such learning consists of statistical emulation of the me...

Simultaneous inference for monotone and smoothly time-varying functions under complex temporal dynamics

Tianpai Luo, Weichi Wu

Optimal Vintage Factor Analysis with Deflation Varimax

Xin Bing, Xin He, Dian Jin et al.

Identifying the Structure of High-Dimensional Time Series via Eigen-Analysis

Bo Zhang, Jiti Gao, Guangming Pan et al.

Design and analysis of randomized trials to estimate spatio-temporally heterogeneous treatment effects

Samuel I. Watson, Thomas A. Smith

Frequency Domain Statistical Inference for High-Dimensional Time Series

Jonas Krampe, Efstathios Paparoditis

Nonparametric data segmentation in multivariate time series via joint characteristic functions

E T McGonigle, H Cho

Summary Modern time series data often exhibit complex dependence and structural changes that are not easily characterized by shifts in ...

Discovering the Network Granger Causality in Large Vector Autoregressive Models

Yoshimasa Uematsu, Takashi Yamagata

High-Dimensional Knockoffs Inference for Time Series Data

Yingying Fan, Jinchi Lv, Chien-Ming Chi et al.

On the Modeling and Prediction of High-Dimensional Functional Time Series

Qiwei Yao, Jinyuan Chang, Xinghao Qiao et al.