2 papers
stat.ME2026
Mixed-Frequency Time Series Forecasting via Depth-Separable Neural Networks
Yize Wang, Qianqian Zhu, Guodong Li
To better forecast mixed-frequency time series, it is the key to choose a suitable way for frequency alignment. However, the existing methods are all limited to linear transformati…
stat.ME2025
An Efficient and Interpretable Autoregressive Model for High-Dimensional Tensor-Valued Time Series
Yuxi Cai, Lan Li, Yize Wang +1
In autoregressive modeling for tensor-valued time series, Tucker decomposition, when applied to the coefficient tensor, provides a clear interpretation of supervised factor modelin…