A novel methodology for stock investment using high utility episode mining and genetic algorithm

2017 ◽  
Vol 59 ◽  
pp. 303-315 ◽  
Author(s):  
Yu-Feng Lin ◽  
Chien-Feng Huang ◽  
Vincent S. Tseng
Keyword(s):  
Genetic Algorithm ◽  
Episode Mining ◽  
Stock Investment ◽  
High Utility

HUE-Span: Fast High Utility Episode Mining

2019 ◽  
pp. 169-184 ◽  
Author(s):  
Philippe Fournier-Viger ◽  
Peng Yang ◽  
Jerry Chun-Wei Lin ◽  
Unil Yun
Keyword(s):  
Episode Mining ◽  
High Utility

Research on Predicted Model of Least Squares Support Vector Machine Based on Genetic Algorithm

2013 ◽  
Vol 753-755 ◽  
pp. 2875-2881 ◽  
Author(s):  
Huai Lin Dong ◽  
Juan Juan Huang ◽  
Zhu Hua Cai ◽  
Qing Feng Wu
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Least Squares ◽  
Support Vector ◽  
Classification Rate ◽  
Financial Investment ◽  
Stock Investment ◽  
Increasing Trend ◽  
Predicted Model ◽  
Stock Indexes

There is huge amount of data with complex uncertainty in the stock market. Meanwhile, efficient stock prediction is important in financial investment. This paper puts forward a classified and predicted model based on least squares support vector machine (LS-SVM) in the background of stock investment. This model preprocesses the input vector of stock indexes using the method of Wilcoxon symbols test and factor analysis, and determines the parameter of LS-SVM based on the genetic algorithm, after that classifies the stocks based on growth rate, then is trained using the stock sample. At last this paper verifies the model with the samples. It also presents a demo to predict the increasing trend of the stock. The result shows that this model owns favorable predicted ability with high correct classification rate.

High Utility Episode Mining Made Practical and Fast

2014 ◽  
pp. 71-84 ◽  
Author(s):  
Guangming Guo ◽  
Lei Zhang ◽  
Qi Liu ◽  
Enhong Chen ◽  
Feida Zhu ◽  
...  
Keyword(s):  
Episode Mining ◽  
High Utility

An Optimization Model Based on Principal Component Theory and Genetic Algorithm

2015 ◽  
Vol 742 ◽  
pp. 384-389
Author(s):  
Xiao Fei Huang
Keyword(s):  
Genetic Algorithm ◽  
Factor Analysis ◽  
Optimization Model ◽  
Principal Component ◽  
Mathematical Function ◽  
Principal Component Factor Analysis ◽  
Function Model ◽  
Component Factor ◽  
Stock Investment ◽  
Component Theory

Stock investment is risky and beyond fixed rules to forecast precisely. In order to realize proper stocks selection from specified mathematical function model, principal component factor analysis is proposed to rebuild various stocks via its contribution rates so as to extract the principal component factors from the elimination of weak ones. According to the synthesis scoring and ranking, optimized stocks has been selected as valuable targets. Test from Genetic Algorithm to the ranking aforementioned indicates that the rationality and validity of the results.

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