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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...
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,...
Predicting stock prices is an important task of financial time series forecasting, which is of great interest to stock investors, stock traders and applied researchers. Many machine learning techniques have been used in recent times to predict the stock price, including regression algorithms which can be useful tools to provide good accuracy of financial time series forecasting. In this paper, we...
Data-Driven Software Reliability Modeling (DDSRM) is an approach in software reliability prediction problem which only relies on software failure data. There are two kinds of model architecture in this modeling, which are Single-Input Single-Output (SISO) and Multiple-Delayed-Input Single-Output (MDISO). In MDISO architecture, the prediction process involves having multiple inputs from the failure...
In this paper, we focus on the prediction method of building energy consumption time series. The building energy consumption data can be regarded as a time series, which is usually nonlinear and non-stationary. Traditional time series analysis model has lower prediction accuracy. Then the machine learning method, especially support vector regression algorithm always has better performance to deal...
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...
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...
In this paper, for the first time, an ensemble of deep learning belief networks (DBN) is proposed for regression and time series forecasting. Another novel contribution is to aggregate the outputs from various DBNs by a support vector regression (SVR) model. We show the advantage of the proposed method on three electricity load demand datasets, one artificial time series dataset and three regression...
Wind power prediction has received much attention due to the development renewable energy sources using wind power. The paper presents a new approach which is a support vector regression (SVR) based local predictor (LP) with false neighbours filtered (FNF-SVRLP) to undertake short-term wind power perdition. The proposed predication method not only combines the powerful SVR with the reconstruction...
Based on image sequence, a void fraction measurement model of gas-liquid two-phase flow in mini-pipe is developed using support vector regression (SVR) and particle swarm optimization (PSO). A high-speed image acquisition system is constructed to capture dynamic gray image sequence of gas-liquid two-phase flow. The area ratio of gas phase in longitudinal section of the pipe for every image of image...
Wind power prediction is one of the most critical aspects in wind power integration and operation. This paper presents a new approach to a wind power prediction by combining support vector regression (SVR) with a local prediction framework which employs the correlation dimension and mutual information methods used in time-series analysis for data preprocessing. Local prediction makes use of similar...
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...
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,...
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 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,...
Accurate grid resources prediction is crucial for a grid scheduler. In this study, support vector regression (SVR), which is an effective regression algorithm, is applied to grid resource prediction. In order to obtain better prediction performance, SVR's parameters must be selected carefully. Therefore, a particle swarm optimization-based SVR (PSO-SVR) model, in which PSO is used to determine free...
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...
In recent years, Support Vector Regression (SVR) is used widely in predication field, with the advantages of structural risk minimization and strong generalization ability, which acquires good effects. The training characters of SVR model is the essential problem of affecting model accuracy. To solve the problem, this paper puts forward SVR model training method based on wavelet multi-resolution analysis,...
Since the implementation of the new mechanism of Renminbi exchange rate from 2005, the CNY/USD exchange rate fluctuation range has become more greater than before. Therefore, it is very important to control CNY/USD exchange rate risk via prediction. This paper is motivated by evidence that different prediction models can complement each other in approximating data sets, and presents a hybrid prediction...
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