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In this paper, a series of spatiotemporal data is analyzed by a regression method based on the theory of support vector machine (SVM). The support vector regression (SVR) model is used to predict the remote sensing data sets effectively. Firstly, we studied how to build a SVR model for spatiotemporal series prediction, and studied the problems of the test data processing, model parameters selection...
Inflation rate could describe economic growth and it is usually used by policy-maker to determine a monetary policy. The Consumer Price Index (CPI) is one of indicator used to measure inflation rate. Until now, the inflation calculations and CPI prediction are conducted on monthly even though it is now likely to predict them on daily basis by utilizing online commodity price movement. Daily predictions...
The reliability of a product is not only important for customers to choose optimal products, but also necessary for manufacturers to design warranty strategies. While predicting the reliability of products accurately is always difficult. Several arithmetic was developed in the existed literature, such as Poisson models, Kalman filter etc. However, these methods hypotheses the distribution of the model,...
This paper extends the idea of Universum learning to regression problems. We propose new Universum-SVM formulation for regression problems that incorporates a priori knowledge in the form of additional data samples. These additional data samples, or Universum samples, belong to the same application domain as the training samples, but they follow a different distribution. Several empirical comparisons...
This paper discusses the effect of classification in estimating the amount of effort (in man-days) associated with code development. Estimating the effort requirements for new software projects is especially important. As outliers are harmful to the estimation, they are excluded from many estimation models. However, such outliers can be identified in practice once the projects are completed, and so...
In recent years many research works have study the problem of photovoltaic power forecasting because of its importance to grid management and large-scale PV integration. In order to forecast the Photovoltaic power production in the region of Casablanca Morocco, a simple and reliable model based on Support Vector Regression (SVR) and local monitoring data is proposed in this paper. Three models based...
Melt index is considered one of the most important variables in determining chemical product quality and thus reliable prediction of melt index (MI) is essential in practical propylene polymerization processes. In this paper, a fuzzy support vector regression (FSVR) based model for propylene polymerization process is developed to predict the MI of polypropylene from other easily measured process variables...
This work studies how to apply support vector machines in order to forecast the energy consumption of buildings. Usually, support vector regression is implemented using the sequential minimal optimisation algorithm. In this work, an alternative version of that algorithm is used to reduce the execution time. Several experiments were carried out taking into account data measured during one year. The...
Particle size is the key technical index of grinding process, but it is difficult to be measured online directly, and the lab analysis has large-time delay. Combined with the advantage of soft-sensor modeling based on support vector regression (SVR), a soft-sensor approach for particle size of grinding process with SVR is proposed. Firstly, the empirical formulae are used to roughly determine the...
Regression problems on massive data sets are ubiquitous in many application domains including the Internet, earth and space sciences, and aviation. Support vector regression (SVR) is a popular technique for modeling the input-output relations of a set of variables under the added constraint of maximizing the margin, thereby leading to a very generalizable and regularized model. However, for a dataset...
Forecasting water levels on Mekong river is an important problem needed to be studied for flood warning. In this paper, we investigate the application to forecasting of daily water levels at Thakhek station on Mekong river using machine learning models such as LASSO, Random Forests and Support Vector Regression (SVR). Experimental results showed that SVR was able to achieve feasible results, the mean...
The reliability of wave prediction is a crucial issue in coastal, harbor and ocean engineering. Support vector machine (SVM) is an appropriate and suitable method for significant wave height (Hs) prediction due to its best versatility, robustness, and effectiveness. In this present work, only significant wave height (Hs) of previous time steps were used as predictors during the period 01-01-2004 to...
In this paper, a new method for demand forecasting of motor vehicle units is proposed. The method is based on interval type-2 possibilistic fuzzy C-means clustering and a special type of sparse kernel machines known as support vector regression. Interval type-2 possibilistic fuzzy C-means clustering is used to partition the input space. Then, indicator variables are selected. For each cluster of input...
Support Vector Regression (SVR) is a flexible regression method, which can be applied directly to NARMAX system identification models. SVR is a one-step convex optimisation process which attempts to maximise generalisation performance. This paper compares SVR performance with that of multi-layer perceptrons and radial basis function networks for varying numbers of time lags included in the model.
A novel model for privacy-preserving support vector regression (PPSVR for short) on vertically partitioned data is proposed in the paper. The feasibility of the model is proved. Besides, the algorithm for vertically partitioned data is given out. In the privacy preserving data mining, each entity is unwilling to share its group of data or leak the data for various reasons. The proposed PPSVR model...
In controlling biological diseases, it is often more potent to use a combination of agents than using individual ones. However, the number of possible combinations increases exponentially with the number of agents and their concentrations. It is prohibitive to search for effective agent combinations by trial and error as biological systems are complex and their responses to agents are often a slow...
We present a new machine learning approach to flash flood forecasting in the absence of rainfall forecasts, based on the agglomerative hierarchical clustering of flood events. Each cluster contains events whose models have similar behaviors. Specific Support Vector Regression models are then trained from each cluster. The test results show that a specific model may be more accurate than a general...
Due to the non-liner, poor selectivity and cross-sensitivity of the combustible gas in the sewer, an analysis prediction model of the combustible gas in the sewer has been established based on the PSO-SVR machine, the model has introduced a new particle swarm algorithm to support the vector regression machine so that it can optimize the important parameters, realizing the automatic determination of...
Today's storage systems and database systems are highly complex and configurable, which makes storage management intricate and costly. One critical aspect of storage management, particularly in large storage infrastructures (e.g. cloud storage), is to determine which application data sets to store on which devices. With a mechanism which has the ability to predict the performance of the storage device...
A trimaran is a multihulled boat consisting of a main hull and two smaller outrigger hulls, attached to the main hull with lateral struts. There have been many studies highlighting that a trimaran takes many advantages over other types of hulls especially the roll performance, because of the greater resistance to rolling that the outrigger hulls offer. In order to study the rolling motion of a trimaran,...
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