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Throughput prediction is one of good solutions to improve quality of mobile applications (e.g., YouTube or Netflix) for video streaming delivery services in mobile networks. This is because such applications require monitoring the network performances to control content quality, thus guarantee quality of service (QoS) and quality of experience (QoE). In this paper, we propose a history-based TCP throughput...
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...
For processing purposes of silver colloidal suspensions in view of specific applications, this study evaluates the suitability of using alginate/lignosulfonate mixtures as an efficient dispersion matrix for the silver nanoparticles. The rheological behavior of the in situ obtained silver nanoparticle suspensions was investigated by rotational measurements performed using cone-plate geometry, considering...
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...
Predicting the deformation based on landslide multi-mode monitor data is a critical issue of reliable data mining and comprehensive knowledge discovering of landslide for early warning. Due to the complex changes of multi-mode monitoring data and interaction effect caused by geological and geomorphological, hydrological, and anthropogenic factors, most of the deformation prediction methods cannot...
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...
Accurate short-term wind power prediction can improve the trade and the dispatch level of wind power. To predict the short-term wind power, we investigated the empirical mode decomposition (EMD) of numerical weather prediction (NWP) and genetic algorithm (GA) optimization of support vector regression (SVR). First, the wind speed data from NWP is decomposed into the EMD components, including multiple...
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 work presents an off-line model, which can provide accurate photovoltaic forecasts for utility grid managers. For this purpose we assess the performance of two models, using well known supervised machine learning techniques, for intra-hour (15 min) solar power forecasting. The first model is created using Least Square Support Vector Regression (LS-SVR) and the second using Feed-forward Neural...
The kernel function's selection has a great impact on the performance of support vector regression (SVR). A new method of nonlinear model predictive control (NMPC) based on polynomial kernel SVR is put forward, and multi-agent particle swarm optimization algorithm is introduced to obtain the optimal control law of rolling optimization in NMPC. Compares with the NMPC based on quadratic polynomial kernel...
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...
The use of solar photovoltaic (PV) in power generation has grown in the last decade. Unlike the traditional power generation methods (i.e. oil and gas), the solar output power is fluctuating and uncertain, mainly due to clouds movement and other weather factors. Therefore, in order to have a stable power grid, the electricity utilities need to forecast the solar output power, so they can prepare ahead...
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,...
In this paper, a blind bandwidth extension algorithm for music signals has been proposed. This method applies the K-means algorithm to firstly cluster audio data in the feature space, and constructs multiple envelope predictors for each cluster accordingly using Support Vector Regression (SVR). A set of well-established audio features for Music Information Retrieval (MIR) has been used to characterize...
In this paper, a prediction-based learning framework is proposed for a continuous prediction task of emotion recognition from speech, which is one of the key components of affective computing in multimedia. The main goal of this framework is to utmost exploit the individual advantages of different regression models cooperatively. To this end, we take two widely used regression models for example,...
This paper proposes a hybrid method for probabilistic wind power forecasting. The proposed approach consists of data classification, deterministic forecasting and probabilistic forecasting stages. In the data classification stage, a fuzzy k-means clustering algorithm is used to classify the historical time series of wind power into various wind classes. Several support vector regression (SVR) models...
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...
We consider the problem of ligand affinity prediction as a regression task, typically with few labelled examples, many unlabelled instances, and multiple views on the data. In chemoinformatics, the prediction of binding affinities for protein ligands is an important but also challenging task. As protein-ligand bonds trigger biochemical reactions, their characterisation is a crucial step in the process...
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...
Dimensional sentiment analysis approach, which represents affective states as continuous numerical values on multiple dimensions, such as valence-arousal (VA) space, allows for more fine-grained analysis than the traditional categorical approach. In recent years, it has been applied in applications such as antisocial behavior detection, mood analysis and product review ranking. In this approach, an...
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