International Journal of Computer Science and Business Informatics
In this paper the properties of Support Vector Machines (SVM) on the financial time series data has been analyzed. The high dimensional stock data consists of many features or attributes. Most of the attributes of features are uninformative for classification. Detecting trends of stock market data is a difficult task as they have complex, nonlinear, dynamic and chaotic behavior. To improve the forecasting of stock data performance different models can be combined to increase the capture of different data patterns.