Research on financial time series forecasting based on SVM
2016pp. 346–349
Citations Over TimeTop 18% of 2016 papers
Abstract
The support vector machine (SVM) is a machine learning method developed based on statistical learning theory. The SVM is widely used in classification and prediction. Since the financial time series is complex, the traditional forecasting methods are less reliable. In this paper, we research on financial time series forecasting based on the support vector machine. Although the speed of prediction process is slow, it can improve the prediction accuracy of the financial time series. The experimental results show the prediction accuracy of this approach based on the support vector machine.
Related Papers
- → Support vector machines, import vector machines and relevance vector machines for hyperspectral classification — A comparison(2011)29 cited
- Research and Application of Support Vector Machine(2004)
- Radiation Source Threat Assessment based on Support Vector Machine(2008)
- → Prediction of Data Classification Based on Support Vector Machine(2016)1 cited
- The Support Vector Machine(SVM) Technique and Its Application(2006)