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 language | English |
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Article number | 1850017 |
Journal | Fluctuation and Noise Letters |
Volume | 17 |
Issue number | 2 |
Early online date | 9 Mar 2018 |
DOIs | |
Publication status | Published - 1 Jun 2018 |
Externally published | Yes |
Keywords
- Basic SSA
- Imputation
- L-SSA
- Missing value
- Reconstruction
- Time series
ASJC Scopus subject areas
- General Mathematics
- General Physics and Astronomy