Novel hybrid SVM-TLBO forecasting model incorporating dimensionality reduction techniques
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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.
KeywordsSupport Vector Machine (SVM) Teaching-Learning-Based Optimization (TLBO) Dimensional reduction Commodity futures contract Financial time series
We would like to express our gratitude to the National Institute of Science and Technology (NIST), for the facilities and resources provided at the Data Science Laboratory at NIST for the development of this study.
Compliance with ethical standards
Conflict of interests
The authors declare that there are no conflict of interests (either financial or non-financial) regarding the publication of the paper.
- 1.Cai LJ, Zhang JQ, Zongwu CAI, Kian Guan LIM (2006) An empirical study of dimensionality reduction in support vector machine. Neural Network World 16(3):177–192Google Scholar
- 3.Cao LJ, Chua KS, Chong WK, Lee HP, Gu QM (2003) A comparison of PCA, KPCA and ICA for dimensional reduction in support vector machines. Neurocomputing 55(1):321–336Google Scholar
- 5.Chang CC, Lin CJ (2011) LIBSVM: A library for support vector machines. ACM Trans Intell Syst Technol (TIST) 2(3):27Google Scholar
- 8.Das SP, Padhy S (2015) A novel hybrid model using teaching–learning-based optimization and a support vector machine for commodity futures index forecasting. Int J Mach Learn Cyber:1–15. doi: 10.1007/s13042-015-0359-0
- 10.Ekenel HK Sankur B (2004) Feature selection in the independent component subspace for face recognition. Pattern Recogn Lett 25(12):377–1388Google Scholar
- 11.Haykin S (2010) Neural Networks and Learning Machines. 3rd Edition, PHI Learning Private LimitedGoogle Scholar
- 20.Jolliffe IT (2002) Principle components analysis 2 nd Edition. Springer, New YorkGoogle Scholar
- 28.Lin HT, Lin CJ (2003) A study on sigmoid kernels for SVM and the training of non-PSD kernels by SMO-type methods Technical report, University of National Taiwan Department of Computer Science and Information Engineering, March 1–32Google Scholar
- 35.Porikli F, Haga T (2004) Event detection by eigenvector decomposition using object and frame features. IEEE Conference In Computer Vision and Pattern Recognition Workshop 2004(CVPRW’04):114–114Google Scholar