Model-free prediction of time series: a nonparametric approach

Mohammad Mohammadi and Meng Li

Journal of Nonparametric Statistics, 2024, vol. 36, issue 3, 804-824

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|

Date: 2024
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DOI: 10.1080/10485252.2023.2266740

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