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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...
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...
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 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,...
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...
In this article, a comparison study of three artificial intelligence (AI) techniques for energy consumption estimation are presented. The models considered are: multilayer perceptron (MLP); radial basis function (RBF) and support vector machine (SVM). The energy consumption is modeled as a function of activity, structural and intensity changes. The models are applied to Canadian industrial manufacturing...
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...
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...
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...
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...
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...
In recent times, gold has been one of the prioritized commodities in terms of long term as well as short term investments since the investors consider gold as a hedgerow against the unforeseen events leading to chaos in the market. Consequentially, the price of gold in the market plays an important role. In this research work, time-series gold price prediction models have been developed using the...
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...
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