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An Investigation of Time Series Models for Forecasting Mixed Migration Flows: Focusing in Germany

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Listed:
  • Vasiliki Mebelli

    (International Hellenic University)

  • Maria Drakaki

    (International Hellenic University)

  • Panagiotis Tzionas

    (International Hellenic University)

Abstract

Refugee and migrant (mixed migration) flows in the Mediterranean have been in the spotlight of both policy and research, especially since 2015. Mixed migration is a volatile international phenomenon with considerable and debatable impacts on society and economy. This paper investigates the performance of time series forecasting methods based on EUROSTAT datasets focusing on asylum seekers. Germany has been selected to reflect on the ability of the models to predict the future behavior of an extremely volatile migrant mobility. Exponential smoothing and autoregressive integrated moving average (ARIMA) models have been used for the forecasting of asylum seekers. Monthly records of first-time asylum seekers have been used from January of 2008 up to September of 2020. The results demonstrate clearly that more research is needed on this field, taking into account the complexity of the characteristics of international migration, in order to assist decision-making in migration management.

Suggested Citation

  • Vasiliki Mebelli & Maria Drakaki & Panagiotis Tzionas, 2023. "An Investigation of Time Series Models for Forecasting Mixed Migration Flows: Focusing in Germany," SN Operations Research Forum, Springer, vol. 4(2), pages 1-11, June.
    • Handle: RePEc:spr:snopef:v:4:y:2023:i:2:d:10.1007_s43069-023-00212-9
    DOI: 10.1007/s43069-023-00212-9
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    References listed on IDEAS

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