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  <title>DSpace Collection:</title>
  <link rel="alternate" href="http://localhost:8080/xmlui/handle/123456789/1063" />
  <subtitle />
  <id>http://localhost:8080/xmlui/handle/123456789/1063</id>
  <updated>2026-04-26T08:07:02Z</updated>
  <dc:date>2026-04-26T08:07:02Z</dc:date>
  <entry>
    <title>Robust Prediction of Stock Indices using PSO based Adaptive Linear Combiner</title>
    <link rel="alternate" href="http://localhost:8080/xmlui/handle/123456789/1668" />
    <author>
      <name>Majhi, Ritanjali</name>
    </author>
    <author>
      <name>Panda, G.</name>
    </author>
    <author>
      <name>Majhi, Babita</name>
    </author>
    <id>http://localhost:8080/xmlui/handle/123456789/1668</id>
    <updated>2024-11-22T05:13:13Z</updated>
    <published>2009-01-01T00:00:00Z</published>
    <summary type="text">Title: Robust Prediction of Stock Indices using PSO based Adaptive Linear Combiner
Authors: Majhi, Ritanjali; Panda, G.; Majhi, Babita
Abstract: The present paper employs a particle swarm&#xD;
optimization (PSO) based adaptive linear combiner for efficient&#xD;
prediction of various stock indices in presence of strong outliers&#xD;
in the training data. The connecting weights of the model are&#xD;
updated by minimizing the Wilcoxon norm of the error vector&#xD;
by PSO. The short and long term prediction performance of&#xD;
the new model is evaluated with test data and the results&#xD;
obtained are compared with those obtained from the&#xD;
conventional PSO based model. It is in general observed that&#xD;
the proposed model is computationally more efficient,&#xD;
prediction wise more accurate and more robust against outliers&#xD;
in training set compared to those obtained by standard PSO&#xD;
based model.
Description: NITW</summary>
    <dc:date>2009-01-01T00:00:00Z</dc:date>
  </entry>
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