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Model-free prediction of time series: a nonparametric approach

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  • Mohammad Mohammadi
  • Meng Li

Abstract

We propose a novel approach for model-free time series forecasting. Unlike most existing methods, the proposed method does not rely on parametric error distributions nor assume parametric forms of the mean function, leading to broad applicability. We achieve such generality by establishing a simple but powerful representation of a time series $ \{X_t;t\in \mathbb {Z}\} $ {Xt;t∈Z} with $ \sup _tE|X_t| \lt \infty $ suptE|Xt|

Suggested Citation

  • Mohammad Mohammadi & Meng Li, 2024. "Model-free prediction of time series: a nonparametric approach," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 36(3), pages 804-824, July.
    • Handle: RePEc:taf:gnstxx:v:36:y:2024:i:3:p:804-824
    DOI: 10.1080/10485252.2023.2266740
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