Deep functional factor models: forecasting high-dimensional functional time series via Bayesian nonparametric factorization

Yirui Liu, Xinghao Qiao, Yulong Pei and Liying Wang

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

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.

JEL-codes: C1 (search for similar items in EconPapers)
Pages: 19 pages
Date: 2024-07-21
New Economics Papers: this item is included in nep-cmp, nep-ecm, nep-ets and nep-for
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Published in Proceedings of Machine Learning Research, 21, July, 2024, 235, pp. 31709-31727. ISSN: 2640-3498

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Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:125587

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