Fusion model of wavelet transform and adaptive neuro fuzzy inference system for stock market prediction

2019 ◽  
Author(s):  
S. Kumar Chandar
Keyword(s):  
Wavelet Transform ◽  
Stock Market ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Fusion Model ◽  
Stock Market Prediction ◽  
Inference System ◽  
Neuro Fuzzy

International transmission of stock market movements: an adaptive neuro-fuzzy inference system for analysis of TAIEX forecasting

2013 ◽  
Vol 23 (S1) ◽  
pp. 369-378 ◽  
Author(s):  
Mu-Yen Chen ◽  
Da-Ren Chen ◽  
Min-Hsuan Fan ◽  
Tai-Ying Huang
Keyword(s):  
Stock Market ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
International Transmission ◽  
Inference System ◽  
Neuro Fuzzy

scholarly journals HYBRID MODEL OF SELF-ORGANIZING MAP AND ADAPTIVE NEURO FUZZY INFERENCE SYSTEM IN STOCK INDEXES FORECASTING

2021 ◽  
Vol 9 (2) ◽  
pp. 70-80
Author(s):  
M. Kushnir ◽  
K. Tokarieva
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Stock Prices ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Financial Time Series ◽  
Self Organizing Map ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Self Organizing

The paper investigates methods of artificial intelligence in the prognostication and analysis of financial data time series. It is uncovered that scholars and practitioners face some difficulties in modelling complex system such as the stock market because it is nonlinear, chaotic, multi- dimensional, and spatial in nature, making forecasting a complex process. Models estimating nonstationary financial time series may include noise and errors. The relationship between the input and output parameters of the models is essentially non-linear, where stock prices include higher-level variables, which complicates stock market modeling and forecasting. It is also revealed that financial time series are multidimensional and they are influenced by many factors, such as economics, politics, environment and so on. Analysis and evaluation of multi- dimensional systems and their forecasting should be carried out by machine learning models. The problem of forecasting the stock market and obtaining quality forecasts is an urgent task, and the methods and models of machine learning should be the main mathematical tools in solving the above problems. First, we proposed to use self-organizing map, which is used to visualize multidimensional data by configuring neurons to quantize or cluster the input space in the topological structure. These characteristics of this algorithm make it attractive in solving many problems, including clustering, especially for forecasting stock prices. In addition, the methods discussed, encourage us to apply this cluster approach to present a different data structure for forecasting. Thus, models of adaptive neuro-fuzzy inference system combine the characteristics of both neural networks and fuzzy logic. Given the fact that the rule of hybrid learning and the theory of logic is a clear advantage of adaptive neuro-fuzzy inference system, which has computational advantages over other methods of parameter identification, we propose a new hybrid algorithm for integrating self-organizing map with adaptive fuzzy inference system to forecast stock index prices. This algorithm is well suited for estimating the relationship between historical prices in stock markets. The proposed hybrid method demonstrated reduced errors and higher overall accuracy.

scholarly journals High Impedance Fault Detection in MV Distribution Network using Discrete Wavelet Transform and Adaptive Neuro-Fuzzy Inference System

2018 ◽  
Author(s):  
Veerapandiyan Veerasamy ◽  
Noor Izzri Abdul Wahab ◽  
Rajeswari Ramachandran ◽  
Muhammad Mansoor ◽  
Mariammal Thirumeni
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Discrete Wavelet ◽  
Inference System ◽  
High Impedance ◽  
Neuro Fuzzy ◽  
High Impedance Fault ◽  
Anfis Classifier

This paper presents a method to detect and classify the high impedance fault that occur in the medium voltage distribution network using discrete wavelet transform (DWT) and adaptive neuro-fuzzy inference system (ANFIS). The network is designed using Matlab software and various faults such as high impedance, symmetrical and unsymmetrical fault have been applied to study the effectiveness of the proposed ANFIS classifier method. This is achieved by training the ANFIS classifier using the features (standard deviation values) extracted from the three phase fault current signal by DWT technique for various cases of fault with different values of fault resistance in the system. The success and discrimination rate obtained for identifying and classifying the high impedance fault from the proffered method is 100% whereas the values are 66.7% and 85% respectively for conventional fuzzy based approach. The results indicate that the proposed method is more efficient to identify and discriminate the high impedance fault accurately from other power system faults in the system.

scholarly journals Damage Detection of Irregular Plates and Regular Dams by Wavelet Transform Combined Adoptive Neuro Fuzzy Inference System

2018 ◽  
Vol 4 (2) ◽  
pp. 305 ◽  
Author(s):  
D. Hamidian ◽  
J. Salajegheh ◽  
E. Salajegheh
Keyword(s):  
Finite Element ◽  
Wavelet Transform ◽  
Damage Detection ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Damage Zone ◽  
Detection Methods ◽  
Mode Shapes ◽  
Inference System ◽  
Neuro Fuzzy

This paper presents a technique for irregular plate and regular dam damage detection based on combination of wavelet with adoptive neuro fuzzy inference system (ANFIS). Many damage detection methods need response of structures (such as the displacements, stresses or mode shapes) before and after damage, but this method only requires response of structures after damage, otherwise many damage detection methods study regular plate but this method also studies irregular plate. First, the structure (irregular plate or regular dam) is modelled by using ANSYS software, the model is analysed and structure’s responses with damage are obtained by finite element approach. Second, the responses at the finite element points with regular distances are obtained by using ANFIS. The damage zone is represented as the elements with reduced elasticity modules. Then these responses of structures are analysed with 2D wavelet transform. It is shown that matrix detail coefficients of 2D wavelet transform can specified the damage zone of plates and regular dams by perturbation in the damaged area.

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