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A robust approach for outlier imputation: Singular Spectrum Decomposition

Author

Listed:
  • 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)

  • Rangan Gupta

    (University of Pretoria, Department of Economics, South Africa)

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.

Suggested Citation

  • Maryam Movahedifar & Hossein Hassani & Masoud Yarmohammadi & Mahdi Kalantari & Rangan Gupta, 2021. "A robust approach for outlier imputation: Singular Spectrum Decomposition," Working Papers 202164, University of Pretoria, Department of Economics.
    • Handle: RePEc:pre:wpaper:202164
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    Keywords

    Outlier; trajectory matrix; signal extraction; singular spectrum analysis and singular spectrum decomposition;
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