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In this paper, the car sales prediction model is established by using Support Vector Regression (SVR) combined with Particles Swarm Optimization algorithm (PSO-SVR). In this model, PSO Algorithm is used to optimize the 3 parameter used in Support Vector Regression. PSO algorithm not only has a strong global search capability, but also solved the problem of over-fitting. Moreover, Mean Absolute Percentage...
Relevance vector machines have been successfully used in many domains, while their application in software reliability prediction is still quite rare. In this work, we propose to apply support vector regression (SVR) to build software reliability prediction model (RVMSRPM). We also compare the prediction accuracy of software reliability prediction models based on RVM, SVM, ANN and three traditional...
Coordinated development degree of county socio-economic system analysis and prediction play an important role in urban agglomeration coordinated development and improve benefit of regional coordinated development in China. According to the county socio-economic system data which is large scale and imbalance, this paper presented a support vector machine model to predict coordinated development degree...
A predictive model of water-quality, which based on wavelet transform and support vector machine, is proposed. This model uses wavelet transform to get water time sequence variations in different scale, and optimizes three parameters of Regression Support Vector Machine with improved Particle Swarm Optimization algorithm, to improve the accuracy of prediction model. This model is used to take one-step...
Least Squares Support Vector Machine (LS-SVM) is a classic algorithm for regression estimation and classification. But unfortunately, for really large problems, LS-SVM can become highly memory and time consuming. In this paper, we present a simplified algorithm for LS-SVM, called ILS-SVM, which effectively reduces the algorithmic complexity. In order to improve the rate of convergence and overcome...
Prediction on complex time series has received much attention during the last decades. Global model is the main tool for time series predicting during the last decades, but it suffers low prediction efficiency, low prediction accuracy and high computation complexity for model training and updating. In recent years, local model for time series prediction draws widely attention for its more accuracy...
Bus travel time prediction is a vital part for both bus operation optimizing system and information service system. This paper reviews existing bus travel time prediction models and analyzes the strengths and weaknesses of each model. A bus travel time prediction model based on nu - Support Vector Regression is proposed, which uses the departure time of bus from origin stop that can reflect traffic...
This paper investigates the use of machine learning to predict a sensitive gait parameter based on acceleration information from previous gait cycles. We investigate a k-step look-ahead prediction which attempts to predict gait variable values based on acceleration information in the current gait cycle. The variable is the minimum toe clearance which has been demonstrated to be a sensitive falls risk...
In the analysis of predicting financial distress based on support vector regression (SVR), irrelevant or correlated features in the samples could spoil the performance of the SVR classifier, leading to decrease of prediction accuracy. In order to solve the problems mentioned above, this paper used rough sets as a preprocessor of SVR to select a subset of input variables and employed the immune clone...
In this paper, we proposed a novel house prediction model that integrated hybrid genetic-based support vector regression (HGA-SVR) model and feng shui theories for developing a high accuracy appraising real estate price system in Taiwan. In Taiwan, feng shui theory applies in choosing good days, divination and house selection. From the past researches, many factors might affect the real estate price...
Support vector machine is a new machine learning technique developed on the basis of statistical learning theory, which has become the hotspot of machine learning because of its excellent learning performance. Based on analyzing the theory of support vector machine for regression (SVR), a SVR model is established for predicting the output in fully mechanized mining face, and then realizes the model...
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...
Support vector regression (SVR) is now a well-established method for non-stationary series forecasting, because of its good generalization ability and guaranteeing global minima. However, only using SVR hardly get satisfied accuracy for complicated frequency spectrum prediction in frequency monitor system (FMS) of high frequency radar. Empirical mode decomposition (EMD) is perfectly suitable for nonlinear...
A multiple support vector regression (SVR) model with time lags was proposed for short term traffic flow prediction. Time lags between current traffic flow and upstream traffic flow were estimated in order to make better use of spatial-temporal correlation between the upstream and the downstream. The time lags could help identify the upstream flow series most similar to that of the current road and...
A number of different forecasting methods have been proposed for traffic flow 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 short-term...
In this paper, a general prediction methodology is proposed which can provide a good service to the related investigations in probabilistic prediction. In particular, the proposed model has the ability to deal with both the deterministic prediction and probabilistic prediction of noisy time series. By means of the proposed approach, local nu-support vector regression (L-nu-SVR) model is exploited...
An approach of a mean hourly wind speed conformal prediction in wind farm is proposed. Conformal prediction is a new prediction methodology. It can be used not just to make predictions but also to estimate the confidence under the usual independent and identically distributed assumption. Based on support vector regression, wind speed regions are predicted by inductive confidence machine. Wind speed...
One of the challenging problems in grid environment is the choice of destination nodes where the tasks of the application are to be executed. Therefore, resource prediction is a crucial direction for job scheduling system and grid users. In this paper, Nu-support vector regression (v-SVR) is applied to solve resource prediction problem. The method of parallel multidimensional step search is also introduced...
Recently, Epsilon-Insensitive Support Vector Regression (epsiv SVR) has been introduced to solve regression and prediction problems. However, the preprocessing of data set and the selection of parameters can become a real computational burden to developer and user. Improper parameters usually lead to prediction performance degradation. In this paper, by introducing Parallel Multidimensional Step Search...
Coordinated signal control can improve the continuity of vehicular traffic flow movement and reduce delay. Cycle flow profile is the base for calculating coordinated signal control parameters. Platoon dispersion characteristic determines the cycle flow profile. So, improving platoon dispersion prediction accuracy can obtain significant benefit for signal coordination. When the velocities of the vehicles...
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