19 citations · 20 across the 7 of their papers we have counts for
9 papers
CP-factorization for high dimensional tensor time series and double projection iterations
Jinyuan Chang, Guanglin Huang, Qiwei Yao +1
We adopt the canonical polyadic (CP) decomposition to model high-dimensional tensor time series. Our primary goal is to identify and estimate the factor loadings in the CP decompos…
Hedging Memory Horizons for Non-Stationary Prediction via Online Aggregation
Yutong Wang, Yannig Goude, Qiwei Yao
We study online prediction under distribution shift, where inputs arrive chronologically and outcomes are revealed only after prediction. In this setting, predictors must remain st…
Spatio-Temporal Autoregressions for High Dimensional Matrix-Valued Time Series
Baojun Dou, Jing He, Sudhir Tiwari +1
Motivated by predicting intraday trading volume curves, we consider two spatio-temporal autoregressive models for matrix time series, in which each column may represent daily tradi…
Testing independence and conditional independence in high dimensions via coordinatewise Gaussianization
Jinyuan Chang, Yue Du, Jing He +1
We propose new statistical tests, in high-dimensional settings, for testing the independence of two random vectors and their conditional independence given a third random vector. T…
HDTSA: An R package for high-dimensional time series analysis
Jinyuan Chang, Jing He, Chen Lin +1
High-dimensional time series analysis has become increasingly important in fields such as finance, economics, and biology. The two primary tasks for high-dimensional time series an…
Identification and estimation for matrix time series CP-factor models
Jinyuan Chang, Yue Du, Guanglin Huang +1
We propose a new method for identifying and estimating the CP-factor models for matrix time series. Unlike the generalized eigenanalysis-based method of Chang et al. (2023) for whi…
On the modelling and prediction of high-dimensional functional time series
Jinyuan Chang, Qin Fang, Xinghao Qiao +1
We propose a two-step procedure to model and predict high-dimensional functional time series, where the number of function-valued time series is large in relation to the length…
Autoregressive networks with dependent edges
Jinyuan Chang, Qin Fang, Eric D. Kolaczyk +2
We propose an autoregressive framework for modelling dynamic networks with dependent edges. It encompasses models that accommodate, for example, transitivity, degree heterogenenity…
An autocovariance-based learning framework for high-dimensional functional time series
Jinyuan Chang, Cheng Chen, Xinghao Qiao +1
Many scientific and economic applications involve the statistical learning of high-dimensional functional time series, where the number of functional variables is comparable to, or…