Prediction financial time series (stock index price) is the most challenging task. Support vector regression (SVR), Support vector machine (SVM) and back propagation neural network (BPNN) are the most popular data mining techniques in prediction financial time series. In this paper a hybrid combination model is introduced to combine the three models and to be most beneficial of them all. Quantization factor is used in this paper for the first time to improve the single SVM and SVR prediction output. And also genetic algorithm (GA) used to determine the weights of the proposed model. FTSE100 daily index closing price is used to evaluate the proposed model performance. The proposed hybrid model numerical results shows the outperform result ov...
Accurate stock market prediction models can provide investors with convenient tools to make better d...
The motivation of this article is to introduce a novel hybrid Genetic algorithm–Support Vector Machi...
[[abstract]]By integrating genetic algorithm (GA)-based optimal time-scale feature extractions with ...
Prediction of financial time series is described as one of the most challenging tasks of time series...
This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University...
Abstract Predicting stock market price is considered as a challenging task of financial time series ...
Abstract: Time series forecasting is receiving remarkable attention from the research community in u...
Many different time-series methods have been widely used in forecast stock prices for earning a prof...
Forecasting stock market prices is an exciting knowledge area for investors and traders. Successful ...
Many different time-series methods have been widely used in forecast stock prices for earning a prof...
[[abstract]]By integrating genetic algorithm (GA)-based optimal time-scale feature extractions with ...
In this study, a prediction model based on support vector machines (SVM) improved by introducing a v...
[[abstract]]Stock market price index prediction is regarded as a challenging task of the financial t...
Abstract. In this study, a hybrid intelligent data mining methodology, genetic algorithm based suppo...
With technological development, trading in stock markets has become more accessible to the general p...
Accurate stock market prediction models can provide investors with convenient tools to make better d...
The motivation of this article is to introduce a novel hybrid Genetic algorithm–Support Vector Machi...
[[abstract]]By integrating genetic algorithm (GA)-based optimal time-scale feature extractions with ...
Prediction of financial time series is described as one of the most challenging tasks of time series...
This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University...
Abstract Predicting stock market price is considered as a challenging task of financial time series ...
Abstract: Time series forecasting is receiving remarkable attention from the research community in u...
Many different time-series methods have been widely used in forecast stock prices for earning a prof...
Forecasting stock market prices is an exciting knowledge area for investors and traders. Successful ...
Many different time-series methods have been widely used in forecast stock prices for earning a prof...
[[abstract]]By integrating genetic algorithm (GA)-based optimal time-scale feature extractions with ...
In this study, a prediction model based on support vector machines (SVM) improved by introducing a v...
[[abstract]]Stock market price index prediction is regarded as a challenging task of the financial t...
Abstract. In this study, a hybrid intelligent data mining methodology, genetic algorithm based suppo...
With technological development, trading in stock markets has become more accessible to the general p...
Accurate stock market prediction models can provide investors with convenient tools to make better d...
The motivation of this article is to introduce a novel hybrid Genetic algorithm–Support Vector Machi...
[[abstract]]By integrating genetic algorithm (GA)-based optimal time-scale feature extractions with ...