DF2M: An Explainable Deep Bayesian Nonparametric Model for
  High-Dimensional Functional Time Series
Yirui Liu Xinghao Qiao Yulong Pei Liying Wang
In this paper, we present Deep Functional Factor Model (DF2M), a Bayesian
nonparametric model for analyzing high-dimensional functional time series. The
DF2M makes use of the Indian Buffet Process and the multi-task Gaussian Process
with a deep kernel function to capture non-Markovian and nonlinear temporal
dynamics. Unlike many black-box deep learning models, the DF2M provides an
explainable way to use neural networks by constructing a factor model and
incorporating deep neural networks within the kernel function. Additionally, we
develop a computationally efficient variational inference algorithm for
inferring the DF2M. Empirical results from four real-world datasets demonstrate
that the DF2M offers better explainability and superior predictive accuracy
compared to conventional deep learning models for high-dimensional functional
time series.