Predicting Oil and Gas Spot Prices Using Chaos Time Series Analysis and Fuzzy Neural Network Model.

2003 ◽  
Author(s):  
I.S. Agbon ◽  
J.C. Araque
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Time Series Analysis ◽  
Network Model ◽  
Neural Network Model ◽  
Fuzzy Neural Network ◽  
Oil And Gas ◽  
Spot Prices ◽  
Series Analysis ◽  
Fuzzy Neural

scholarly journals PEMODELAN VOLATILITAS RETURN PORTOFOLIO SAHAM MENGGUNAKAN FEED FORWARD NERURAL NETWORK (Studi Kasus :PT Bumi Serpong Damai Tbk. Dan PT H.M Sampoerna Tbk.)

2018 ◽  
Vol 7 (2) ◽  
pp. 200-211
Author(s):  
Rizki Pradipto Widyantomo ◽  
Abdul Hoyyi ◽  
Tatik Widiharih
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Time Series Analysis ◽  
Network Model ◽  
Neural Network Model ◽  
Arima Model ◽  
Garch Model ◽  
Residual Variance ◽  
Series Analysis ◽  
Input Variables

Time series analysis is an analysis used to predict a time-observed data, one of which is the ARIMA model. ARIMA model assumes a constant residual variance (homogeneous). While financial data usually produce ARIMA model with variance error that is not constant. If the assumption of homogeneity of the residual variance is not met, then the method that can be used is ARCH or GARCH model. Another method that can be used on the data assuming the homogeneity of the variance error is not met is the Neural Network model. In this model we use Neural Network model with variance and residual as the input variables that obtained from ARCH / GARCH model. The data used are BSDE and HMSP asset portfolio returns from November 14, 2016 to January 18, 2018. In this study the selected input variables are from ARIMA (1.0.1) GARCH (1,1) model. The best Neural Network model obtained is Neural Network model with 10 hidden layers with MSE value 6.58 x10-10 with model train evaluation which is MAPE value 1.14441%.Keywords: Time series Analysis, ARCH / GARCH, Neural Network, Return.

A dynamic relearning neural network model for time series analysis of online marine data

2014 ◽  
Vol 73 ◽  
pp. 99-107
Author(s):  
Wenqing Li ◽  
Wenyan Wang ◽  
Xiaoyan Wang ◽  
Shixuan Liu ◽  
Liang Pei ◽  
...  
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Time Series Analysis ◽  
Network Model ◽  
Neural Network Model ◽  
Series Analysis ◽  
Marine Data

Ramp Metering Research of Junction Based on Fuzzy Neural Network Model

2011 ◽  
Vol 11 (1) ◽  
pp. 108-113 ◽  
Author(s):  
Feng CHEN ◽  
Yuanhua JIA ◽  
Zhonghai NIU ◽  
Huixin YI ◽  
Huijuan SONG
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Fuzzy Neural Network ◽  
Ramp Metering ◽  
Fuzzy Neural

Application of T-S Fuzzy Neural Network in the Assessment of River Ecosystem Health

2013 ◽  
Vol 726-731 ◽  
pp. 958-962 ◽  
Author(s):  
Zhen Chun Hao ◽  
Xiao Li Liu ◽  
Qin Ju
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Fuzzy System ◽  
Ecosystem Health ◽  
Fuzzy Neural Network ◽  
Evaluation Model ◽  
Fuzzy Model ◽  
River Ecosystem ◽  
Fuzzy Neural

Healthy river ecosystem has been acknowledged as the object of river management, which is crucial for the sustainable development of cities. Simple and practical evaluation methods with great precision are necessary for the evaluation of river ecosystem health. Fuzzy system has been widely used in evaluation and decision making for its simple reasoning and the adoption of experts knowledge. However, much artificial intervention decreases the precision. Neural network has a strong ability of self-leaning while it is not good at expressing rule-based knowledge. The T-S fuzzy neural network model combines the advantages of fuzzy system and neural network. In this paper, the T-S fuzzy neural network model was used to establish a river ecosystem health evaluation model. Results show that the combination of T-S fuzzy model and neural network eliminates the influences of subjective factors and improve the final precisions efficiently.

scholarly journals Modelling of the Slope Solute Loss Based on Fuzzy Neural Network Model

2022 ◽  
Vol 42 (2) ◽  
pp. 677-688
Author(s):  
Xiaona Zhang ◽  
Jie Feng ◽  
Zhen Hong ◽  
Xiaona Rui
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Fuzzy Neural Network ◽  
Fuzzy Neural

RAOGA-Based Fuzzy Neural Network Model of Design Evaluation

2006 ◽  
pp. 206-211 ◽  
Author(s):  
Li-Hua Xue ◽  
Hong-Zhong Huang ◽  
Jun Hu ◽  
Qiang Miao ◽  
Dan Ling
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Fuzzy Neural Network ◽  
Design Evaluation ◽  
Fuzzy Neural

scholarly journals Application of T-S Fuzzy-Neural Network Model in Water Quality Comprehensive Evaluation

2020 ◽  
Vol 166 ◽  
pp. 501-506 ◽  
Author(s):  
Xian Qun Jiang ◽  
Wu Fen Chen ◽  
Li Jun Guo ◽  
Zheng Wu Huang
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Network Model ◽  
Neural Network Model ◽  
Fuzzy Neural Network ◽  
Comprehensive Evaluation ◽  
Fuzzy Neural
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