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Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/1238

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contributor.authorMajhi, R-
contributor.authorPanda, G-
contributor.authorMajhi, B-
identifier.citation2009 World Congress on Nature and Biologically Inspired Computing, NABIC 2009; Coimbatore; 9 December 2009 through 11 December 2009; Category number CFP0995H; Code 79534; Article number 5393728, Pages 312-317en
description.abstractThe present paper employs a particle swarm optimization (PSO) based adaptive linear combiner for efficient prediction of various stock indices in presence of strong outliers in the training data. The connecting weights of the model are updated by minimizing the Wilcoxon norm of the error vector by PSO. The short and long term prediction performance of the new model is evaluated with test data and the results obtained are compared with those obtained from the conventional PSO based model. It is in general observed that the proposed model is computationally more efficient, prediction wise more accurate and more robust against outliers in training set compared to those obtained by standard PSO based model.en
format.extent645681 bytes-
subjectAdaptive linear combiner;en
subjectError vector;en
subjectLong-term prediction;en
subjectNew model;en
subjectStandard PSO;en
subjectStock indices;en
subjectTest data;en
subjectTraining data;en
subjectTraining setsen
titleRobust Prediction of Stock Indices using PSO based Adaptive Linear Combineren
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