Zobrazeno 1 - 10
of 428
pro vyhledávání: '"Tsay, Ruey S."'
Missing data often significantly hamper standard time series analysis, yet in practice they are frequently encountered. In this paper, we introduce temporal Wasserstein imputation, a novel method for imputing missing data in time series. Unlike exist
Externí odkaz:
http://arxiv.org/abs/2411.02811
Vector AutoRegressive Moving Average (VARMA) models form a powerful and general model class for analyzing dynamics among multiple time series. While VARMA models encompass the Vector AutoRegressive (VAR) models, their popularity in empirical applicat
Externí odkaz:
http://arxiv.org/abs/2406.19702
Autor:
Huang, Shuo-Chieh, Tsay, Ruey S.
We propose a novel approach for time series forecasting with many predictors, referred to as the GO-sdPCA, in this paper. The approach employs a variable selection method known as the group orthogonal greedy algorithm and the high-dimensional Akaike
Externí odkaz:
http://arxiv.org/abs/2406.09625
Autor:
Gao, Zhaoxing, Tsay, Ruey S.
Ridge regression is an indispensable tool in big data analysis. Yet its inherent bias poses a significant and longstanding challenge, compromising both statistical efficiency and scalability across various applications. To tackle this critical issue,
Externí odkaz:
http://arxiv.org/abs/2405.00424
Denoising and Multilinear Projected-Estimation of High-Dimensional Matrix-Variate Factor Time Series
Autor:
Gao, Zhaoxing, Tsay, Ruey S.
This paper proposes a new multi-linear projection method for denoising and estimation of high-dimensional matrix-variate factor time series. It assumes that a $p_1\times p_2$ matrix-variate time series consists of a dynamically dependent, lower-dimen
Externí odkaz:
http://arxiv.org/abs/2309.02674
Autor:
Gao, Zhaoxing, Tsay, Ruey S.
Publikováno v:
Journal of the American Statistical Association, 2024
This paper proposes a novel dynamic forecasting method using a new supervised Principal Component Analysis (PCA) when a large number of predictors are available. The new supervised PCA provides an effective way to bridge the gap between predictors an
Externí odkaz:
http://arxiv.org/abs/2307.07689
Autor:
Huang, Shuo-Chieh, Tsay, Ruey S.
Feature-distributed data, referred to data partitioned by features and stored across multiple computing nodes, are increasingly common in applications with a large number of features. This paper proposes a two-stage relaxed greedy algorithm (TSRGA) f
Externí odkaz:
http://arxiv.org/abs/2307.03410
This paper proposes a new approach to identifying the effective cointegration rank in high-dimensional unit-root (HDUR) time series from a prediction perspective using reduced-rank regression. For a HDUR process $\mathbf{x}_t\in \mathbb{R}^N$ and a s
Externí odkaz:
http://arxiv.org/abs/2304.12134