scholarly journals Construction of ANFIS Model Based on LM-Test for Forecasting of Chili Price Data in Semarang

2020 ◽  
Author(s):  
Tarno Tarno ◽  
Di Asih I Maruddani ◽  
Rita Rahmawati
Keyword(s):  
Anfis Model ◽  
Price Data ◽  
Model Based ◽  
Lm Test

A hybrid ANFIS model based on AR and volatility for TAIEX forecasting

2011 ◽  
Vol 11 (1) ◽  
pp. 1388-1395 ◽  
Author(s):  
Jing-Rong Chang ◽  
Liang-Ying Wei ◽  
Ching-Hsue Cheng
Keyword(s):  
Anfis Model ◽  
Model Based

scholarly journals Analyzing Stock-Bond Correlation in Emerging Markets

2019 ◽  
Vol 11 (3) ◽  
pp. 83
Author(s):  
Qurat Ul Ain Ehtesham ◽  
Danish Ahmed Siddiqui
Keyword(s):  
Emerging Markets ◽  
White Noise ◽  
Asset Allocation ◽  
Multivariate Garch ◽  
Portfolio Construction ◽  
Conditional Volatility ◽  
Price Data ◽  
Lm Test ◽  
Noise Error ◽  
Diversification Benefit

This study investigates stock-bond correlation in 17 countries of emerging markets during 2011 to 2018 using monthly price data. Data was analyzed using ARCH-LM test, GJR GARCH and Multivariate GARCH type Asymmetric DCC model. Findings of this paper revealed that sequence of return series are stationary containing white noise error, past return volatilities do not have the ability to predict future volatilities and conditional volatility is higher and negative momentum of the market increase the correlation of stock and bond in a country or vice versa and hence increase the diversification benefit for asset allocation in a portfolio construction and provide hedging assets characteristics among countries and it is found that there is a co-movement between stock and bond in a country of emerging markets.

scholarly journals A New ANFIS Model based on Multi-Input Hamacher T-norm and Subtract Clustering

2014 ◽  
Vol 8 (1) ◽  
pp. 833-838 ◽  
Author(s):  
Feng-Yi Zhang ◽  
Zhi-Gao Liao
Keyword(s):  
Empirical Study ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Anfis Model ◽  
Prediction Ability ◽  
Inference System ◽  
Model Based ◽  
Neuro Fuzzy ◽  
Proposed Model

This paper proposed a novel adaptive neuro-fuzzy inference system (ANFIS), which combines subtract clustering, employs adaptive Hamacher T-norm and improves the prediction ability of ANFIS. The expression of multiinput Hamacher T-norm and its relative feather has been originally given, which supports the operation of the proposed system. Empirical study has testified that the proposed model overweighs early work in the aspect of benchmark Box- Jenkins dataset and may provide a practical way to measure the importance of each rule.

Runoff Prediction Using a Novel Hybrid ANFIS Model Based on Variable Screening

2021 ◽  
Author(s):  
Zhennan Liu ◽  
Qiongfang Li ◽  
Jingnan Zhou ◽  
Weiguo Jiao ◽  
Xiaoyu Wang
Keyword(s):  
Anfis Model ◽  
Variable Screening ◽  
Model Based ◽  
Runoff Prediction
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