A Three-Stage Nonparametric Kernel-Based Time Series Model Based on Fuzzy Data

Gholamreza Hesamian, Arne Johannssen () and Nataliya Chukhrova
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Gholamreza Hesamian: Department of Statistics, Payame Noor University, Tehran 19395-3697, Iran
Arne Johannssen: Faculty of Business Administration, University of Hamburg, 20146 Hamburg, Germany
Nataliya Chukhrova: HafenCity University of Hamburg, 20457 Hamburg, Germany

Mathematics, 2023, vol. 11, issue 13, 1-17

Abstract: In this paper, a nonlinear time series model is developed for the case when the underlying time series data are reported by L R fuzzy numbers. To this end, we present a three-stage nonparametric kernel-based estimation procedure for the center as well as the left and right spreads of the unknown nonlinear fuzzy smooth function. In each stage, the nonparametric Nadaraya–Watson estimator is used to evaluate the center and the spreads of the fuzzy smooth function. A hybrid algorithm is proposed to estimate the unknown optimal bandwidths and autoregressive order simultaneously. Various goodness-of-fit measures are utilized for performance assessment of the fuzzy nonlinear kernel-based time series model and for comparative analysis. The practical applicability and superiority of the novel approach in comparison with further fuzzy time series models are demonstrated via a simulation study and some real-life applications.

Keywords: fuzzy regression; fuzzy time series model; nonparametric time series analysis; time series analysis (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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