From the 1 of 11 linked papers with an AI index.
11 papers
Mixed-Frequency Time Series Forecasting via Depth-Separable Neural Networks
Yize Wang, Qianqian Zhu, Guodong Li
The paper proposes a deep neural network architecture that aligns mixed-frequency time series using nonlinear transformations, with parameter sharing across stages to improve forec…
ParaRNN: An Interpretable and Parallelizable Recurrent Neural Network for Time-Dependent Data
Yuxi Cai, Lan Li, Feiqing Huang +1
The proliferation of large-scale and structurally complex data has spurred the integration of machine learning methods into statistical modeling. Recurrent neural networks (RNNs),…
High-dimensional Autoregressive Modeling for Time Series with Hierarchical Structures
Lan Li, Shibo Yu, Yingzhou Wang +1
Modern applications have made ubiquitous high-dimensional data, especially time-dependent data, with more and more complicated structures, and it also has become more frequent to e…
Reduced-Rank Autoregressive Model for High-Dimensional Multivariate Network Time Series
Qi Lyu, Xiaoyu Zhang, Guodong Li +1
Multivariate network time series are ubiquitous in modern systems, yet existing network autoregressive models typically treat nodes as scalar processes, ignoring cross-variable spi…
Improving time series estimation and prediction via transfer learning
Yuchang Lin, Qianqian Zhu, Guodong Li
There are many time series in the literature with high dimension yet limited sample sizes, such as macroeconomic variables, and it is almost impossible to obtain efficient estimati…
High-dimensional low-rank matrix regression with unknown latent structures
Di Wang, Xiaoyu Zhang, Guodong Li +1
We study low-rank matrix regression in settings where matrix-valued predictors and scalar responses are observed across multiple individuals. Rather than assuming a fully homogeneo…