Novel hybrid SVM-TLBO forecasting model incorporating dimensionality reduction techniques
作者： Shom Prasad Das , N. Sangita Achary , Sudarsan Padhy
DOI: 10.1007/S10489-016-0801-3
关键词:
摘要: In this paper, we present a highly accurate forecasting method that supports improved investment decisions. The proposed extends the novel hybrid SVM-TLBO model consisting of support vector machine (SVM) and teaching-learning-based optimization (TLBO) determines optimal SVM parameters, by combining it with dimensional reduction techniques (DR-SVM-TLBO). dimension (feature extraction approach) extract critical, non-collinear, relevant, de-noised information from input variables (features), reduce time complexity. We investigated three different feature techniques: principal component analysis, kernel independent analysis. feasibility effectiveness ensemble were examined using case study, predicting daily closing prices COMDEX commodity futures index traded in Multi Commodity Exchange India Limited. assessed performance new techniques, metrics statistical measures. compared our results standard an model. Our experimental show is viable effective, provides better predictions. This can provide technical for financial decisions be used as alternative tasks require more
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