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Spectral modeling of time series with missing data

Research output: Contribution to journalArticleScientificpeer-review

Details

Original languageEnglish
Pages (from-to)4676-4684
Number of pages9
JournalApplied Mathematical Modelling
Volume37
Issue number7
DOIs
Publication statusPublished - 1 Apr 2013
Publication typeA1 Journal article-refereed

Abstract

Singular spectrum analysis is a natural generalization of principal component methods for time series data. In this paper we propose an imputation method to be used with singular spectrum-based techniques which is based on a weighted combination of the forecasts and hindcasts yield by the recurrent forecast method. Despite its ease of implementation, the obtained results suggest an overall good fit of our method, being able to yield a similar adjustment ability in comparison with the alternative method, according to some measures of predictive performance.

Keywords

  • Karhunen-Loève decomposition, Missing data, Singular spectrum analysis, Time series analysis