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Financial time series prediction is remains a challenge, due to the nonstationary and fuzziness financial data. In this paper, we propose and achieve a hybrid financial time series model by combining the Maximum Entropy (ME), Support Vector Regression (SVR) and Trend model based on Artificial neural networks (ANNs) for forecasting financial time series. The method contains three steps. The first step...
This paper introduced a novel forecasting method, Support Vector Regression with Local Predictor (SVRLP), which aims to forecast the short-term load distribution function. To increase the forecast accuracy, the conventional Support Vector Regression (SVR) is combined with a phase space reconstruction technique, called local predictor. This proposed forecast method can be applied to forecast the load...
Support vector regression (SVR) is a common learning method for machines which is developed these years. Comparing with the other regression models, this method has the advantages of structural risk minimization and strong generalization ability. It is widely used in the prediction field and acquires good effects. The training characters of SVR model are very important to SVR. To solve the problem,...
This paper presents a new approach to short-term wind speed prediction. The chaotic time series analysis method is used to capture the characteristic of complex wind behavior in which a correlation dimension method is employed to calculate embedding dimension of the time series, then a mutual information method is used to determine the time delay. Based on the embedding dimension and time delay, support...
This paper compares the performance of Radial Basis Function and Support Vector Regression in time series forecasting. Both methods were trained to produce one step ahead forecasting on two chaotic time series data: Mackey Glass and Set A data from Santa Fe Competition. The criterions for comparison are based on the coefficient of determination (R2) and Root Mean Square Error (RMSE) between actual...
In this paper the wind speed forecasting in a wind farm, applying the algorithm of support vector regression (SVR) to the mean 10-minute time series is presented. By comparing its performance with an back propagation neural network model through simulation results, we could find following facts: firstly, both algorithms are applicable for prediction the wind speed time series in future; secondly,...
Due to the nonlinear and nonstationary of river water turbidity, a novel hybrid forecasting model based on phase space reconstruction and support vector regression (PSR-SVR) is proposed. Firstly, the embedding dimension is chosen by using the false nearest neighbor method, and the time delay is obtained by the average mutual information. The phase space is reconstructed from the time series with the...
Traffic accident forecasting is important for altering and planning of road. Recently time series analysis is an important direction in traffic accident forecasting. Support vector regression (SVR) a kind of SVM used in regression and has better nonlinear forecasting performance than BP neural network. In the paper, the combination method based on particle swarm optimization and support vector regression...
This paper proposes a new probabilistic method for maximum temperature forecasting in short-term electrical load forecasting. The proposed method makes use of Gaussian process (GP)of the kernel machine to evaluate the predicted temperature. In recent years, electric power markets become more deregulated and competitive. The power system players are concerned with maximizing a profit while minimizing...
A number of different forecasting methods have been proposed for cooling load forecasting including historic method, real-time method, time series analysis, and artificial neural networks (ANN), but accuracy and time efficiency in prediction are a couple of contradictions to be hard to resolve for real-time traffic information prediction. In order to improve time efficiency of prediction, a new hourly...
Due to the fluctuation and complexity of the financial time series, it is difficult to use any single artificial technique to capture its non-stationary property and accurately describe its moving tendency. So a novel hybrid intelligent forecasting model based on empirical mode decomposition (EMD) and support vector regression (SVR) is proposed. EMD can adaptively decompose the complicated raw data...
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...
Forecasting electricity consumption is an important index for system planning, operation and decision making. In order to improve the accuracy of the forecasting, we apply an integrated architecture to optimize the prediction. Based on an integration of two machine learning techniques: artificial fish swarm algorithm search approach based on test-sample error estimate criterion (AFSAS-TEE) and support...
As a learning mechanic, support vector machine (SVMs) has been studied and applied in a wide area. This study deals with the special futures of SVM in predicting the total workload in telecommunication. The contributions include: (a) Building a predicted model of the total workload in telecommunications and predicting using it; (b)Analyzing the parameter of support vector regression(SVRs) which influence...
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