An Exponential Autoregressive Time Series Model for Complex Data

被引：1

作者：

Hesamian, Gholamreza ^{[1
]}

Torkian, Faezeh ^{[1
]}

Johannssen, Arne ^{[2
]}

Chukhrova, Nataliya ^{[2
]}

机构：

[1] Payame Noor Univ, Dept Stat, Tehran 193953697, Iran

[2] Univ Hamburg, Fac Business Adm, D-20146 Hamburg, Germany

来源：

MATHEMATICS | 2023年 / 11卷 / 19期

关键词：

AR model; ARMA model; fuzzy nonlinear time series; fuzzy data; time series analysis; REGRESSION-MODEL; HYBRID MODEL; FORECASTING-MODEL; FUZZY;

D O I：

10.3390/math11194022

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

In this paper, an exponential autoregressive model for complex time series data is presented. As for estimating the parameters of this nonlinear model, a three-step procedure based on quantile methods is proposed. This quantile-based estimation technique has the benefit of being more robust compared to least/absolute squares. The performance of the introduced exponential autoregressive model is evaluated by means of four established goodness-of-fit criteria. The practical utility of the novel time series model is showcased through a comparative analysis involving simulation studies and real-world data illustrations.

引用

页数：12

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