Uncovering delayed patterns in noisy and irregularly sampled time series: An astronomy application

Juan C. CUEVAS-TELLO, Peter TIŇO, Somak RAYCHAUDHURY, Xin YAO, Markus HARVA

Research output: Journal PublicationsJournal Article (refereed)peer-review

17 Citations (Scopus)

Abstract

We study the problem of estimating the time delay between two signals representing delayed, irregularly sampled and noisy versions of the same underlying pattern. We propose and demonstrate an evolutionary algorithm for the (hyper)parameter estimation of a kernel-based technique in the context of an astronomical problem, namely estimating the time delay between two gravitationally lensed signals from a distant quasar. Mixed types (integer and real) are used to represent variables within the evolutionary algorithm. We test the algorithm on several artificial data sets, and also on real astronomical observations of quasar Q0957+561. By carrying out a statistical analysis of the results we present a detailed comparison of our method with the most popular methods for time delay estimation in astrophysics. Our method yields more accurate and more stable time delay estimates. Our methodology can be readily applied to current state-of-the-art optical monitoring data in astronomy, but can also be applied in other disciplines involving similar time series data. © 2009 Elsevier Ltd. All rights reserved.
Original languageEnglish
Pages (from-to)1165-1179
Number of pages15
JournalPattern Recognition
Volume43
Issue number3
Early online date13 Aug 2009
DOIs
Publication statusPublished - Mar 2010
Externally publishedYes

Keywords

  • Evolutionary algorithms
  • Kernel regression
  • Mixed representation
  • Statistical analysis
  • Time series

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