Time-stamped cross-sectional data, which lack linkage across time points, are commonly
generated in single-cell transcriptional profiling. Many previous methods for inferring gene …

As a special infinite-order vector autoregressive (VAR) model, the vector autoregressive
moving average (VARMA) model can capture much richer temporal patterns than the widely …

This article presents the development of a geo-visualization tool, which provides police
officers or any other type of law enforcement officer with the ability to conduct the …

Tensor and matrix time series data have been amassed more and more from many areas in
recent years, calling for new statistical models, methods and algorithms for analyzing such …

Vector AutoRegressive Moving Average (VARMA) models form a powerful and general
model class for analyzing dynamics among multiple time series. While VARMA models …

This paper presents a Wald test for multi-horizon Granger causality within a high-
dimensional sparse Vector Autoregression (VAR) framework. The null hypothesis focuses …

Spectral precision matrix, the inverse of a spectral density matrix, is an object of central
interest in frequency-domain analysis of multivariate time series. Estimation of spectral …

This contribution shows our ongoing work aimed at monitoring societal issues and economic
activities (eg, industrial production, unemployment, loneliness, cultural participation) across …

High-dimensional time series datasets are becoming increasingly common in many areas of
biological and social sciences. Some important applications include gene regulatory …

We propose a stochastic volatility model for time series of curves. It is motivated by dynamics
of intraday price curves that exhibit both between days dependence and intraday price …