A robust approach for outlier imputation: Singular Spectrum Decomposition

Maryam Movahedifar, Hossein Hassani (), Masoud Yarmohammadi, Mahdi Kalantari and Rangan Gupta
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Maryam Movahedifar: Department of Statistics, Payame Noor University, 19395-4697, Tehran, Iran
Hossein Hassani: Institute for International Energy Studies, Tehran 1967743 711, Iran
Masoud Yarmohammadi: Department of Statistics, Payame Noor University, 19395-4697, Tehran, Iran
Mahdi Kalantari: Department of Statistics, Payame Noor University, 19395-4697, Tehran, Iran

No 202164, Working Papers from University of Pretoria, Department of Economics

Abstract: Singular Spectrum Analysis (SSA) is a nonparametric method for separating time series data into a sum of small numbers of interpretable components (signal+noise). One of the steps of the SSA method, which is referenced to Embedding, is extremely sensitive to contamination of outliers which are often founded in time series analysis. To reduce the effect of outliers, SSA based on Singular Spectrum Decomposition (SSD) method is proposed. In this paper the ability of SSA based on SSD and basic SSA are compared in time series reconstruction in the presence of outliers. A wide empirical study on both simulated and real data verifies the efficiency of basic SSA based on SSD in reconstructing the time series where polluted by outliers.

Keywords: Outlier; trajectory matrix; signal extraction; singular spectrum analysis and singular spectrum decomposition (search for similar items in EconPapers)
Pages: 10 pages
Date: 2021-09
New Economics Papers: this item is included in nep-isf, nep-mac and nep-ore
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Persistent link: https://EconPapers.repec.org/RePEc:pre:wpaper:202164

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