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Stock market forecasting has attracted a lot of research interests in previous literature, and recent studies have shown that artificial neural networks (ANN) method achieved better performance than traditional statistical ones. ANN approaches have, however, suffered from difficulties with generalization, producing models that can overfit the data. This paper employs a relatively new machine learning...
As a branch of data mining, data classification technology has got a widely use in science, engineering, finance and other areas. The key point of the classification techniques is to construct a classifier, in this paper, a non-liner classifier model based on RBF neural network is introduced to do the data classification, compared with traditional BP neural network, it is not only avoids complicated...
Accelerated Degradation Testing (ADT) is now adopted frequently to verify the reliability and life of high-reliable, long-life product. But ADT data analysis methods are still deficiency. Due to the excellent capable of little sample learning and nonlinear mapping, SVM prediction model is widely used in many fields. In this paper, a new degradation prediction method based on Support Vector Machines...
The theory of support vector regression (SVR) is introduced in this paper. And genetic algorithms (GAs) are adopted to optimize free parameters of support vector regression. Then we develop an optimal meteorological prediction model based on support vector Regression with genetic algorithms (SVRG). In this study, SVRG is applied to predict meteorology. The experimental results indicate that SVRG model...
Stock index prediction seems to be a challenging task of the financial time series prediction process especially in emerging markets with their complex and inefficient structures. Multivariate adaptive regression splines (MARS) is a nonlinear and non-parametric regression methodology and has been successfully used in classification tasks. However, there are few applications using MARS in stock index...
The main objective of this study is to develop a predictor variable selection method based on rough set theory (RST) for runoff prediction, according to the different influence of different climate variables in different grid point on the runoff. The selected predictor variables were used as downscaling analysis predictors. Multiple linear regression (MLR), back propagation neural network (BPNN) and...
This paper studies the relation between chlorophyll-a and 10 environmental factors such as water temperature (T), COD, NH4+, NO3- TN, PO43+, TP, suspend solids (SS), Secci-depth (SD) and water depth (D) based on the monitoring data of 2005 in Taihu Lake. Three kinds of models are designed using the multiple regression statistical (MRS) method, the back propagation artifical neural network (BP ANN)...
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