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This paper deals with modelling high-volatile time series using modern machine learning technique called Support Vector Regression. After discussing the basic principles of Support Vector Machines (SVM), we construct SVM Regression Prediction Model. Afterwards, this prediction SVR model is applied to oil prices. Due to high-volatile and dynamic character these data are very difficult to model. Experiments...
Multi-step ahead forecasting is an important issue for organizations, often used to assist in tactical decisions. Such forecasting can be achieved by adopting time series forecasting methods, such as the classical Holt-Winters (HW) that is quite popular for seasonal series. An alternative forecasting approach comes from the use of more flexible learning algorithms, such as Neural Networks (NN) and...
Accurate building cooling load forecasting is the precondition for the optimal control and energy saving operation of HVAC systems. Hourly cooling load forecasting is a difficult work as the load at a given point is dependent not only on the load at the previous hour but also on the load at the same hour on the previous day. So the accuracy of forecasting is influenced by many unpredicted factors...
Due to time series forecasting involves a rather complex data pattern, there are lots of novel forecasting approaches to improve the forecasting accuracy. Unlike most conventional neural network models, which are based on the empirical risk minimization principle, SVM applies the structural risk minimization principle to minimize an upper bound of the generalization error, rather than minimizing the...
The forecast on the time series of the parameter -varying chaotic system using LS-SVM was researched in this paper. The SVM method is built on the structural risk minimum theory. The least square support vector machine (LS-SVM) is one kind of SVM, which solvers the problem using the equal restriction because of adopting the quadratic loss function. The LS-SVM holds the virtue of classical SVM and...
Economic growth forecasting is important to make the policy on national economic development. Support vector machine (SVM) is a new machine learning method, which seeks to minimize an upper bound of the generalization error instead of the empirical error as in conventional neural networks. In the study, support vector machine and particle swarm optimization is applied in economic growth forecasting,...
Forecasting the tax gross exactly is significant to carry on the macroscopic regulation efficiently under the market economy. Conventional linear macroscopic economic model is very difficult to hold non-linear phenomena in economic system, thus the tax forecasting error will increase. Support vector machine (SVM) has been successfully employed to solve regression problem of nonlinearity and small...
Chaotic time series analysis or forecasting is an important and complex problem in machine learning. As an effective tool, support vector machine (SVM) has been broadly adopted in pattern recognition and machine learning fields. In developing a successful SVM classifier, eliminating noise and extracting feature are very important. This paper proposes the application of kernel PCA to LS-SVM for feature...
Accurate forecasting of electricity load has been one of the most important issues in the electricity industry. Recently, along with power system privatization and deregulation, accurate forecast of electricity load has received increasing attention. According to the chaotic and non-linear characters of power load data, the model of support vector machines (SVM) based on Lyapunov exponents was established...
In blast furnace (BF) ironmaking process, silicon content in hot metal is an important index, which reflects the thermal state of BF. To predict the silicon content in hot metal effectively and level up the forecasting accuracy, a novel combined model based on empirical mode decomposition (EMD) and support vector machine (SVM) is proposed. Firstly, the time series data of silicon content in hot metal...
As an effective tool in pattern recognition and machine learning, support vector machine (SVM) has been adopted abroad. In developing a successful SVM classifier, eliminating noise and extracting feature are very important. This paper proposes the application of kernel PCA to SVM for feature extraction. Then PSO Algorithm is adopted to optimization of these parameters in SVM. The novel time series...
Electricity price forecasting is a difficult yet essential task for market participants in a deregulated electricity market. Rather than forecasting the value, market participants are sometimes more interested in forecasting the prediction interval of the electricity price. Forecasting the prediction interval is essential for estimating the uncertainty involved in the price and thus is highly useful...
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