Time Series Imputation via L1 Norm-Based Singular Spectrum Analysis

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Kalantari, Y and Yarmohammadi, M and Hassani, H. and Silva, E.S. (2018) Time Series Imputation via L1 Norm-Based Singular Spectrum Analysis. Fluctuation and Noise Letters, 17 (2).

Type of Research:	Article
Creators:	Kalantari, Y and Yarmohammadi, M and Hassani, H. and Silva, E.S.
Description:	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.
Keywords/subjects not otherwise listed:	Time series; basic SSA; L1-SSA; reconstruction; missing value; imputation.
Your affiliations with UAL:	Colleges > London College of Fashion
Date:	9 March 2018
Digital Object Identifier:	10.1142/S0219477518500177
Date Deposited:	27 Apr 2018 10:43
Last Modified:	19 Feb 2026 11:41
Item ID:	12574
URI:	https://ualresearchonline.arts.ac.uk/id/eprint/12574
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