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Sparse Multiple Index Modelsfor High-dimensional Nonparametric Forecasting

Author

Listed:
  • Nuwani K Palihawadana
  • Rob J Hyndman
  • Xiaoqian Wang

Abstract

Forecasting often involves high-dimensional predictors which have nonlinear relationships with theoutcome of interest. Nonparametric additive index models can capture these relationships, while addressing the curse of dimensionality. This paper introduces a new algorithm, Sparse Multiple Index (SMI) Modelling, tailored for estimating high-dimensional nonparametric/semi-parametric additive index models, while limiting the number of parameters to estimate, by optimising predictor selectionand predictor grouping. The SMI Modelling algorithm uses an iterative approach based on mixed integer programming to solve an L0-regularised nonlinear least squares optimisation problem withlinear constraints. We demonstrate the performance of the proposed algorithm through a simulation study, along with two empirical applications to forecast heat-related daily mortality and daily solarintensity.

Suggested Citation

  • Nuwani K Palihawadana & Rob J Hyndman & Xiaoqian Wang, 2024. "Sparse Multiple Index Modelsfor High-dimensional Nonparametric Forecasting," Monash Econometrics and Business Statistics Working Papers 16/24, Monash University, Department of Econometrics and Business Statistics.
    • Handle: RePEc:msh:ebswps:2024-16
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    File URL: https://www.monash.edu/business/ebs/research/publications/ebs/2024/wp16-2024.pdf
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    Keywords

    Additive Index Models; Variable Selection; Dimension Reduction; Predictor Grouping; Mixed Integer Programming;
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