Deep Functional Factor Models: Forecasting High-Dimensional Functional Time Series via Bayesian Nonparametric Factorization
Abstract
This paper introduces the Deep Functional Factor Model (DF2M), a Bayesian nonparametric model designed for analysis of high-dimensional functional time series. DF2M is built upon the Indian Buffet Process and the multi-task Gaussian Process, incorporating a deep kernel function that captures non-Markovian and nonlinear temporal dynamics. Unlike many black-box deep learning models, DF2M offers an explainable approach to utilizing neural networks by constructing a factor model and integrating deep neural networks within the kernel function. Additionally, we develop a computationally efficient variational inference algorithm to infer DF2M. Empirical results from four real-world datasets demonstrate that DF2M provides better explainability and superior predictive accuracy compared to conventional deep learning models for high-dimensional functional time series.
Cite
Text
Liu et al. "Deep Functional Factor Models: Forecasting High-Dimensional Functional Time Series via Bayesian Nonparametric Factorization." International Conference on Machine Learning, 2024.Markdown
[Liu et al. "Deep Functional Factor Models: Forecasting High-Dimensional Functional Time Series via Bayesian Nonparametric Factorization." International Conference on Machine Learning, 2024.](https://mlanthology.org/icml/2024/liu2024icml-deep/)BibTeX
@inproceedings{liu2024icml-deep,
title = {{Deep Functional Factor Models: Forecasting High-Dimensional Functional Time Series via Bayesian Nonparametric Factorization}},
author = {Liu, Yirui and Qiao, Xinghao and Pei, Yulong and Wang, Liying},
booktitle = {International Conference on Machine Learning},
year = {2024},
pages = {31709-31727},
volume = {235},
url = {https://mlanthology.org/icml/2024/liu2024icml-deep/}
}