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

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  • Liu, Yirui
  • Qiao, Xinghao
  • Pei, Yulong
  • Wang, Liying

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.

Suggested Citation

  • Liu, Yirui & Qiao, Xinghao & Pei, Yulong & Wang, Liying, 2024. "Deep functional factor models: forecasting high-dimensional functional time series via Bayesian nonparametric factorization," LSE Research Online Documents on Economics 125587, London School of Economics and Political Science, LSE Library.
    • Handle: RePEc:ehl:lserod:125587
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    References listed on IDEAS

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    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General

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