Sensitivity analysis of economic variables using neuro-fuzzy approach

被引：0
|
作者：
Lotfi E. [1 ]
Babrzadeh S. [2 ]
Khosravi A. [3 ]
机构：
[1] Department of Computer Engineering, Torbat-e Jam Branch, Islamic Azad University, Torbat-e Jam
[2] Department of Computer Engineering and Information Technology, Payam-e Noor University, Asalooye
[3] Center for Intelligence Systems Research, Deakin University, Geelong
来源：
Scientia Iranica | 2020年 / 27卷 / 3 D期
关键词：
Economic management; Economic time series; Fuzzy forecast; Sensitivity analysis; Soft computing;
D O I：
10.24200/SCI.2019.5488.1305
中图分类号：
学科分类号：
摘要：
Sensitivity Analysis (SA) is an essential requirement for decision-making in economic management. In this paper, a novel Fuzzy Sensitivity Analyzer (FSA) is proposed to analyze the sensitivity of economic variables. The proposed FSA algorithm consists of an Adaptive Neuro-Fuzzy Inference System (ANFIS) that is adjusted for forecasting economic time series. Based on the output of ANFIS, FSA can determine the importance degree of parameters. In the numerical studies, the proposed method is applied to carry out the sensitivity analysis of oil and gold time series. According to the results, FSA indicates that oil price is highly dependent upon the inflation rate, dollar index, and market index, while OPEC production level and gold price are of low impact. Furthermore, in the gold price modeling, the highest sensitivity is obtained from silver price, while demand for gold is a function of market index and inflation rate. The proposed method can be used in many SA applications. © 2020 Sharif University of Technology. All rights reserved.
引用
收藏
页码：1352 / 1359
页数：7
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