Log In Sign Up

A two-stage architecture for stock price forecasting by combining SOM and fuzzy-SVM

by   Duc-Hien Nguyen, et al.

This paper proposed a model to predict the stock price based on combining Self-Organizing Map (SOM) and fuzzy-Support Vector Machines (f-SVM). Extraction of fuzzy rules from raw data based on the combining of statistical machine learning models is base of this proposed approach. In the proposed model, SOM is used as a clustering algorithm to partition the whole input space into the several disjoint regions. For each partition, a set of fuzzy rules is extracted based on a f-SVM combining model. Then fuzzy rules sets are used to predict the test data using fuzzy inference algorithms. The performance of the proposed approach is compared with other models using four data sets


page 1

page 2

page 3

page 4


Improving the Interpretability of Support Vector Machines-based Fuzzy Rules

Support vector machines (SVMs) and fuzzy rule systems are functionally e...

Improved Accuracy of PSO and DE using Normalization: an Application to Stock Price Prediction

Data Mining is being actively applied to stock market since 1980s. It ha...

Rainfall-runoff prediction using a Gustafson-Kessel clustering based Takagi-Sugeno Fuzzy model

A rainfall-runoff model predicts surface runoff either using a physicall...

Diagnosis of Coronary Artery Disease Using Artificial Intelligence Based Decision Support System

This research is about the development a fuzzy decision support system f...

Fuzzy Granular-Ball Computing Framework and Its Implementation in SVM

Most existing fuzzy computing methods use points as input, which is the ...

Adaptive Neuro Fuzzy Networks based on Quantum Subtractive Clustering

Data mining techniques can be used to discover useful patterns by explor...