Time series imputation via L1 norm-based singular spectrum analysis

Mahdi Kalantari, Masoud Yarmohammadi, Hossein Hassani, Emmanuel Sirimal Silva

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

Abstract

Missing values in time series data is a well-known and important problem which many researchers have studied extensively in various fields. In this paper, a new nonparametric approach for missing value imputation in time series is proposed. The main novelty of this research is applying the L1 norm-based version of Singular Spectrum Analysis (SSA), namely L1-SSA which is robust against outliers. The performance of the new imputation method has been compared with many other established methods. The comparison is done by applying them to various real and simulated time series. The obtained results confirm that the SSA-based methods, especially L1-SSA can provide better imputation in comparison to other methods.
Original languageEnglish
Article number1850017
JournalFluctuation and Noise Letters
Volume17
Issue number2
Early online date9 Mar 2018
DOIs
Publication statusPublished - 1 Jun 2018
Externally publishedYes

Keywords

  • Basic SSA
  • Imputation
  • L-SSA
  • Missing value
  • Reconstruction
  • Time series

ASJC Scopus subject areas

  • General Mathematics
  • General Physics and Astronomy

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