Maximizing Correlation in the Presence of Missing Data

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Abstract

In this paper we address the problem of maximizing the correlation between two vectors of time series data, when one of the vectors has missing data and the timing of the missing data is unknown. The motivation for this work comes from environmental monitoring where because of monitoring malfunction, some data are lost. We study the use of integer programming and a genetic algorithm (GA) for this problem.

Original languageAmerican English
JournalApplied Mathematical Sciences
Volume2
StatePublished - Jan 1 2008

Keywords

  • Genetic algorithm
  • Integer programming
  • Missing data
  • combinatorial optimization

DC Disciplines

  • Business Administration, Management, and Operations
  • Operations and Supply Chain Management

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