Long-term forecasting in financial stock market using accelerated LMA on neuro-fuzzy structure and additional fuzzy C-Means clustering for optimizing the GMFs.

2008 ◽  
Author(s):  
Felix Pasila ◽  
Sautma Ronni ◽  
Thiang ◽  
Lie Handra Wijaya
Keyword(s):  
Stock Market ◽  
Fuzzy C Means ◽  
Neuro Fuzzy ◽  
Fuzzy C Means Clustering ◽  
Long Term Forecasting

An Evolutionary Neuro-Fuzzy C-means Clustering Technique

2020 ◽  
Vol 89 ◽  
pp. 103435 ◽  
Author(s):  
Priyanka D. Pantula ◽  
Srinivas S. Miriyala ◽  
Kishalay Mitra
Keyword(s):  
Clustering Technique ◽  
Fuzzy C Means ◽  
Neuro Fuzzy ◽  
Fuzzy C Means Clustering

scholarly journals Spatial modeling of long-term air temperatures for sustainability: evolutionary fuzzy approach and neuro-fuzzy methods

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e8882
Author(s):  
Abolghasem Sadeghi-Niaraki ◽  
Ozgur Kisi ◽  
Soo-Mi Choi
Keyword(s):  
Absolute Error ◽  
Geographical Information ◽  
Subtractive Clustering ◽  
Fuzzy Methods ◽  
Air Temperatures ◽  
Neuro Fuzzy ◽  
Fuzzy C Means Clustering ◽  
Fuzzy Genetic ◽  
Gp Model

This paper investigates the capabilities of the evolutionary fuzzy genetic (FG) approach and compares it with three neuro-fuzzy methods—neuro-fuzzy with grid partitioning (ANFIS-GP), neuro-fuzzy with subtractive clustering (ANFIS-SC), and neuro-fuzzy with fuzzy c-means clustering (ANFIS-FCM)—in terms of modeling long-term air temperatures for sustainability based on geographical information. In this regard, to estimate long-term air temperatures for a 40-year (1970–2011) period, the models were developed using data for the month of the year, latitude, longitude, and altitude obtained from 71 stations in Turkey. The models were evaluated with respect to mean absolute error (MAE), root mean square error (RMSE), Nash–Sutcliffe efficiency (NSE), and the determination coefficient (R2). All data were divided into three parts and every model was tested on each. The FG approach outperformed the other models, enhancing the MAE, RMSE, NSE, and R2 of the ANFIS-GP model, which yielded the highest accuracy among the neuro-fuzzy models by 20%, 30%, and 4%, respectively. A geographical information system was used to obtain temperature maps using estimates of the optimal models, and the results of the model were assessed using it.

scholarly journals Risk Mapping of Cutaneous Leishmaniasis via a Fuzzy C Means-based Neuro-Fuzzy Inference System

2014 ◽  
Vol XL-2/W3 ◽  
pp. 19-23
Author(s):  
P. Akhavan ◽  
M. Karimi ◽  
P. Pahlavani
Keyword(s):  
Data Mining ◽  
Cutaneous Leishmaniasis ◽  
Fuzzy Inference System ◽  
Prevalence Rate ◽  
Fuzzy Systems ◽  
Fuzzy Inference ◽  
Inference System ◽  
Fuzzy C Means ◽  
Neuro Fuzzy ◽  
Fuzzy C Means Clustering

Finding pathogenic factors and how they are spread in the environment has become a global demand, recently. Cutaneous Leishmaniasis (CL) created by Leishmania is a special parasitic disease which can be passed on to human through phlebotomus of vector-born. Studies show that economic situation, cultural issues, as well as environmental and ecological conditions can affect the prevalence of this disease. In this study, Data Mining is utilized in order to predict CL prevalence rate and obtain a risk map. This case is based on effective environmental parameters on CL and a Neuro-Fuzzy system was also used. Learning capacity of Neuro-Fuzzy systems in neural network on one hand and reasoning power of fuzzy systems on the other, make it very efficient to use. In this research, in order to predict CL prevalence rate, an adaptive Neuro-fuzzy inference system with fuzzy inference structure of fuzzy C Means clustering was applied to determine the initial membership functions. Regarding to high incidence of CL in Ilam province, counties of Ilam, Mehran, and Dehloran have been examined and evaluated. The CL prevalence rate was predicted in 2012 by providing effective environmental map and topography properties including temperature, moisture, annual, rainfall, vegetation and elevation. Results indicate that the model precision with fuzzy C Means clustering structure rises acceptable RMSE values of both training and checking data and support our analyses. Using the proposed data mining technology, the pattern of disease spatial distribution and vulnerable areas become identifiable and the map can be used by experts and decision makers of public health as a useful tool in management and optimal decision-making.

Forecasting East Asian Tourist Arrivals to Thailand with Adaptive Neuro-Fuzzy Inference System

2019 ◽  
Vol 4 (1) ◽  
pp. 1-8
Author(s):  
Chawalsak Phetchanchai ◽  
Chuthawuth Chantaramalee ◽  
Napatsarun Chatchawalanont ◽  
Piyapong Phatcha
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
East Asian ◽  
Time Lag ◽  
Training Dataset ◽  
Inference System ◽  
Fuzzy C Means ◽  
Neuro Fuzzy ◽  
Fuzzy C Means Clustering ◽  
Takagi Sugeno

Objective - This research aims to propose the approach of forecasting tourist arrivals to Thailand. Methodology/Technique – Adaptive Neuro-Fuzzy Inference System (ANFIS) was used as our forecasting method by using fuzzy C-means clustering as a technique for the partitioning training dataset Findings - The appropriate parameter of time lag was found for each dataset of East Asian tourist arrivals to Thailand. Novelty - The forecasting procedure with the appropriate parameter of time lag was represented our work as a novelty idea. Type of Paper: Empirical. Keywords: Tourist arrivals forecasting, East Asian countries, adaptive neuro-fuzzy inference system, fuzzy C-means clustering, Takagi–Sugeno fuzzy inference system.

scholarly journals A Weight Possibilistic Fuzzy C-Means Clustering Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Jiashun Chen ◽  
Hao Zhang ◽  
Dechang Pi ◽  
Mehmed Kantardzic ◽  
Qi Yin ◽  
...  
Keyword(s):  
Data Analysis ◽  
Stock Market ◽  
Objective Function ◽  
Clustering Algorithm ◽  
Lagrange Equation ◽  
Experimental Results ◽  
Retail Market ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering

Fuzzy C-means (FCM) is an important clustering algorithm with broad applications such as retail market data analysis, network monitoring, web usage mining, and stock market prediction. Especially, parameters in FCM have influence on clustering results. However, a lot of FCM algorithm did not solve the problem, that is, how to set parameters. In this study, we present a kind of method for computing parameters values according to role of parameters in the clustering process. New parameters are assigned to membership and typicality so as to modify objective function, on the basis of which Lagrange equation is constructed and iterative equation of membership is acquired, so does the typicality and center equation. At last, a new possibilistic fuzzy C-means based on the weight parameter algorithm (WPFCM) was proposed. In order to test the efficiency of the algorithm, some experiments on different datasets are conducted to compare WPFCM with FCM, possibilistic C-means (PCM), and possibilistic fuzzy C-means (PFCM). Experimental results show that iterative times of WPFCM are less than FCM about 25% and PFCM about 65% on dataset X12. Resubstitution errors of WPFCM are less than FCM about 19% and PCM about 74% and PFCM about 10% on the IRIS dataset.

Sign in / Sign up

Export Citation Format

Share Document