Novel hybrid SVM-TLBO forecasting model incorporating dimensionality reduction techniques

In this paper, we present a highly accurate forecasting method that supports improved investment decisions. The proposed method extends the novel hybrid SVM-TLBO model consisting of a support vector machine (SVM) and a teaching-learning-based optimization (TLBO) method that determines the optimal SVM parameters, by combining it with dimensional reduction techniques (DR-SVM-TLBO). The dimension reduction techniques (feature extraction approach) extract critical, non-collinear, relevant, and de-noised information from the input variables (features), and reduce the time complexity. We investigated three different feature extraction techniques: principal component analysis, kernel principal component analysis, and independent component analysis. The feasibility and effectiveness of this proposed ensemble model were examined using a case study, predicting the daily closing prices of the COMDEX commodity futures index traded in the Multi Commodity Exchange of India Limited. In this study, we assessed the performance of the new ensemble model with the three feature extraction techniques, using different performance metrics and statistical measures. We compared our results with results from a standard SVM model and an SVM-TLBO hybrid model. Our experimental results show that the new ensemble model is viable and effective, and provides better predictions. This proposed model can provide technical support for better financial investment decisions and can be used as an alternative model for forecasting tasks that require more accurate predictions.