Recently, the Support Vector Regression (SVR) has been applied in the financial time series prediction. The financial data are usually highly noisy and contain outliers. Detecting outliers and deflating their influence are important but hard problems. In this paper, we propose a novel “two-phase” SVR training algorithm to detect outliers and reduce their negative impact. Our experimental result...

Most works on Support Vector Regression (SVR) focus kernel or loss functions, with the corresponding support vectors obtained using a fixed-radius [Formula: see text]-tube, affording good predictive performance datasets. However, fixed radius limitation prevents adaptive selection of according to data distribution characteristics, compromising SVR-based methods. Therefore, this study proposes a...

Support vector machines are a popular machine learning method for many classification tasks in biology and chemistry. In addition, the support vector regression (SVR) variant is widely used for numerical property predictions. In chemoinformatics and pharmaceutical research, SVR has become the probably most popular approach for modeling of non-linear structure-activity relationships (SARs) and p...

The paper presents the application of support vector regression (SVR) to accurate forecasting of the tangential displacement of a concrete dam. The SVR nonlinear autoregressive model with exogenous inputs (NARX) was developed and tested using experimental data collected during fourteen years. A total of 573 data were used for training of the SVR model whereas the remaining 156 data were used to...

Keywords: Demand forecasting Genetic algorithm–simulated annealing (GA–SA) Support vector regression (SVR) Autoregressive integrated moving average (ARIMA) General regression neural networks (GRNN) Third generation (3G) mobile phone a b s t r a c t Taiwan is one of the countries with higher mobile phone penetration rate in the world, along with the increasing maturity of 3G relevant products, t...

Adapting stimuli to stabilize neural responses is an important problem in the context of cortical prostheses. This paper describes two approaches for stimulus adaptation using support vector regression (SVR). One approach involves the solution of an inverse problem and it is shown that for linear SVR an analytical solution exists. The proposed algorithms are evaluated in conjunction with differ...

In order to better manage and optimize supply chain, a reliable prediction of future demand is needed. The difficulty of forecasting demand is due mainly to the fact that heterogeneous factors may affect it. Analyzing such kind of data by using classical time series forecasting methods, will fail to capture such dependency of factors. This paper is released to present a forecasting approach of ...

Against the problem that indoor positioning suffers quite large errors and irregular user location movement, this paper adopts Support Vector Regression (SVR) for initial positioning and Kalman filtering for filtering of the positioning results so as to improve the accuracy of the positioning system. The experimental results show that against the real WLAN environment, SVR positioning results p...

The empirical risk minimization methods were often used to estimate the multifunctional sensor regression function in signal reconstruction. The small size of sample data would lead to the problem of poor generalization capability and overfitting. Support vector machine (SVM) is a novel machine learning method based on structural risk minimization, and it can improve generalization capability a...

Accurate prediction of the remaining useful life (RUL) of lithium-ion batteries is important for battery management systems. Traditional empirical data-driven approaches for RUL prediction usually require multidimensional physical characteristics including the current, voltage, usage duration, battery temperature, and ambient temperature. From a capacity fading analysis of lithium-ion batteries...