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The paper presents a mathematical model of processing and forecasting time series data. The mathematical model is based on the methods of artificial neural networks and preliminary data processing using wavelet transform. Various classes of algorithms for predicting changes in the parameters of continuous functions and time series that occur in the interval called the prediction horizon are considered...
In order to coordinate the heat supply of heat source and users' heat demand and also maintain the whole system work in a high efficiency, the wavelet analysis was utilized to make short-term prediction on heating load. The heating load of heat supply system was chosen as the output variation. After applying wavelet transform to the heat load sequence, the low-frequency signals and high-frequency...
To accurately predict the non-stationary time series, an approach based on integration of wavelet transform, PSO (Particle Swarm Optimization) and SVM (Support Vector Machine) is proposed. Wavelet decomposition is used to reduce the complexity of time series. Different components are predicted by their corresponding SVM forecasters, respectively, after wavelet transform. The final forecasting result...
In the electricity system, supply and demand must be equal at all times. Wind power generation is fluctuating due to the variation of wind. As more and more wind power generation is integrated into the power system, it is very important to predict the wind power production to contribute the system reserve reduction and the operational costs of the power plants. This paper brings wavelet transform...
In the analysis of predicting power load forecasting based on least squares neural network, the instability of the time series could lead to decrease of prediction accuracy. On the other hand,neural network and chaos theories parameters must be carefully predetermined in establishing an efficient model. In order to solve the problems mentioned above, in this paper, the neural network and chaos theory...
The development of wind generation has rapidly progressed over the last decade, the most important application for wind power prediction is to reduce the need for balancing energy and reserve power, which are needed to integrate wind power into the balancing of supply and demand in the electricity supply system. This paper presents a new method of wind power prediction in short-term with Artificial...
In this paper we use local Holder exponents to capture local patterns in protein sequences. The numerical sequence of a protein based on a 6-letters model of amino acids is considered as a time series, then its local Holder exponents are estimated using the wavelet transform. The probability density of local Holder exponents is then calculated. The probability density values are then taken as features...
In the analysis of predicting share price based on least squares support vector machine (LS-SVM), the instability of the time series could lead to decrease of prediction accuracy. On the other hand, three SVM parameters, c, epsiv and sigma, must be carefully predetermined in establishing an efficient LS-SVM model. In order to solve the problems mentioned above, in this paper, the hybrid of wavelet...
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