In this article, a PSO-SVR-GRNN nonparametric hybrid model is proposed for the CSI 300 stock index to forecast problem. Particle Swarm Optimization (PSO) utilized optimize parameters of SVR enhance prediction ability support vector machine's regression original Index time series. The optimized residual sequence results General Regression Neural Network (GRNN) are then used series prediction. ou...

In this letter, we derive an algorithm that computes the entire solution path of the support vector regression (SVR). We also propose an unbiased estimate for the degrees of freedom of the SVR model, which allows convenient selection of the regularization parameter.

In this paper we derive an algorithm that computes the entire solution path of the support vector regression, with essentially the same computational cost as fitting one SVR model. We also propose an unbiased estimate for the degrees of freedom of the SVR model, which allows convenient selection of the regularization parameter.

Inherent process and measurement uncertainty has posed a challenging issue on soft sensor development of batch bioprocesses. In this paper, a new soft sensor modeling framework is proposed by integrating Bayesian inference strategy with two-stage support vector regression (SVR) method. The Bayesian inference procedure is first designed to identify measurement biases and misalignments via poster...

This paper explores the Support Vector Machine and Least Square Support Vector Machine models in stock forecasting. Three prevailing forecasting techniques General Autoregressive Conditional Heteroskedasticity (GARCH), Support Vector Regression (SVR) and Least Square Support Vector Machine (LSSVM) are combined with the wavelet kernel to form three novel algorithms Wavelet-based GARCH (WL_GARCH)...

various statistical methods have been proposed in terms of predicting the outcomes of facing special factors. in the classical approaches,  making the probability distribution or known probability density functions is ordinarily necessary to predict the desired outcome. however, most of the times enough information about the probability distribution of studied variables is not available to the ...

Forecasting of gas emission rate in mine is a complicated problem due to its nonlinearity and the small quantity of training data. Support vector regression (SVR) can solve the problem with small samples, nonlinear and high dimensions. However, the precision of SVR is significantly affected by its parameter. In order to improve the mine gas emission rate accurately, an optimal selection approac...

This paper explores Support Vector Regression (SVR) as an alternative to the widely-used Support Vector Classification (SVC) in GLDS (Generalized Linear Discriminative Sequence)based speaker verification. SVR allows the use of a ε-insensitive loss function which presents many advantages. First, the optimization of the ε parameter adapts the system to the variability of the features extracted fr...

This paper develops tourism demand econometric models based on the monthly data of tourists to Taiwan and adopts Multivariate Adaptive Regression Splines (MARS), Artificial Neural Network (ANN) and Support Vector Regression (SVR), MARS, ANN and SVR to develop forecast models and compare the forecast results. The results showed that SVR model is the optimal model, with a mean error rate of 3.61%...

In this article, we propose a novel application for Support Vector Regression (SVR) for strategies of executing orders on stock exchanges. We use SVR for predicting volume participation function in execution strategies which try to achieve Volume Weighted Average Price (VWAP) measure of quality of the execution. Moreover, we use SVR with a priori knowledge about stock prices in order to further...