An Intelligent and Dynamic Decision Support System for Nonlinear Environments

Nonlinear time series systems are high dimensional and chaotic in nature. Since, the design of a dynamic and efficient decision making system is a challenging task, a Support Vector Machine (SVM) based model is proposed to predict the future event of a nonlinear time series environment. This model is a non-parametric model that uses the inherent structure of the data for forecasting. The Hybrid Dimensionality Reduction (HDR) and Extended Hybrid Dimensionality Reduction (EHDR) techniques are proposed to represent the time series data and to reduce the dimensionality and control noise besides subsequencing the time series data. The proposed SVM based model using EHDR is compared with the models using Symbolic Aggregate approXimation (SAX), HDR, SVM using Kernel Principal Component Analysis(KPCA) and SVM using varying tube size values for historical data on different financial instruments. The experimental results have proved that the prediction accuracy of the proposed model is better compared with other models taken for the experimentation.