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Support vector regression (SVR) model has been widely applied to time series prediction. Due to the inherent non linearity and non-stationary characteristics of financial time series, conventional modeling techniques such as the Box-Jenkins autoregressive integrated moving average are not adequate for financial time series prediction. In this paper a hybrid model based on modified harmony search algorithm,...
Hydrology time series prediction is significant. It is not only helpful to set the planning in daily configuration works of water resources, but also provides guidance for leaders to make decision, especially in some special case such as flood and seriously lack water. In order to solve the imbalance complexity of prediction model and complexity of samples and raise forecasting accuracy, combined...
Hydrology time series prediction is significant. It is not only helpful to set the planning in daily configuration works of water resources, but also provides guidance for leaders to make decision, especially in some special case such as flood and seriously lack water. In order to solve the imbalance complexity of prediction model and complexity of samples and raise forecasting accuracy, combined...
This study applies a novel neural-network technique, support vector regression (SVR), to predict reliably in dynamical system. The aim of this study is to examine the feasibility of SVR in state prediction by comparing it with the existing neural-network approaches. To build an effective SVR model, SVR's parameters must be set carefully. This study proposes a novel approach, known as GA-SVR, which...
In this study, the application of independent component analysis (ICA), a new feature extraction method, and support vector regression (SVR) in time series prediction is presented. The proposed method first use ICA as preprocessing to transform the input space composed of original time series data into the feature space consisting of independent components (ICs) representing underlying information/features...
It is important to choose good parameters in support vector regression (SVR) modeling. Choosing different parameters will influence the accuracy of SVR models. This paper proposes a parameter choosing method of SVR models for time series prediction. In the light of data features of time series, the paper improves the traditional cross-validation method, and combines the improved cross-validation with...
A time series prediction method using support vector regression (SVR) for machining errors is presented in this paper. The design steps and learning algorithm are also addressed. Since SVR have greater generalization ability and guarantee global minima for given training data, it is believed that SVR will perform well for time series for machining errors. A typical machining process of cutting bearing...
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