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Evaluating the Performance of Multiple Imputation Methods for Handling Missing Values in Time Series Data: A Study Focused on East Africa, Soil-Carbonate-Stable Isotope Data

Author

Listed:
  • Hossein Hassani

    (Research Institute of Energy Management and Planning (RIEMP), University of Tehran, Tehran 1417466191, Iran)

  • Mahdi Kalantari

    (Department of Statistics, Payame Noor University, Tehran 19395-4697, Iran)

  • Zara Ghodsi

    (PHASTAR, London W4 5LE, UK)

Abstract

In all fields of quantitative research, analysing data with missing values is an excruciating challenge. It should be no surprise that given the fragmentary nature of fossil records, the presence of missing values in geographical databases is unavoidable. As in such studies ignoring missing values may result in biased estimations or invalid conclusions, adopting a reliable imputation method should be regarded as an essential consideration. In this study, the performance of singular spectrum analysis (SSA) based on L 1 norm was evaluated on the compiled δ 13 C data from East Africa soil carbonates, which is a world targeted historical geology data set. Results were compared with ten traditionally well-known imputation methods showing L 1 -SSA performs well in keeping the variability of the time series and providing estimations which are less affected by extreme values, suggesting the method introduced here deserves further consideration in practice.

Suggested Citation

  • Hossein Hassani & Mahdi Kalantari & Zara Ghodsi, 2019. "Evaluating the Performance of Multiple Imputation Methods for Handling Missing Values in Time Series Data: A Study Focused on East Africa, Soil-Carbonate-Stable Isotope Data," Stats, MDPI, vol. 2(4), pages 1-11, December.
    • Handle: RePEc:gam:jstats:v:2:y:2019:i:4:p:32-467:d:298325
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    References listed on IDEAS

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