Time series prediction techniques have been used in many real-world applications such as
financial market prediction, electric utility load forecasting, weather and environmental state …

Support vector machines (SVMs) are promising methods for the prediction of financial time-
series because they use a risk function consisting of the empirical error and a regularized …

This paper proposes a modified version of support vector machines (SVMs), called dynamic
support vector machines (DSVMs), to model non-stationary time series. The DSVMs are …

A novel type of learning machine called support vector machine (SVM) has been receiving
increasing interest in areas ranging from its original application in pattern recognition to …

This paper proposes using the support vector machines (SVMs) experts for time series
forecasting. The generalized SVMs experts have a two-stage neural network architecture. In …

In this paper, the problem of optimizing SVR automatically for time series forecasting is
considered, which involves introducing auto-adaptive parameters Ci and εi to depict the non …

This paper proposes a modified version of support vector machines (SVMs), called ε-
descending support vector machines (ε-DSVMs), to model non-stationary financial time …

This paper deals with the application of a novel neural network technique, support vector
machine (SVM), in financial time series forecasting. The objective of this paper is to examine …

The use of Support Vector Machines (SVMs) is studied in financial forecasting by comparing
it with a multi-layer perceptron trained by the Back Propagation (BP) algorithm. SVMs …

Support vector machine is a new tool from Artificial Intelligence (AI) field has been
successfully applied for a wide variety of problem especially in time-series forecasting. In …