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

Vasiliki Mebelli (), Maria Drakaki () and Panagiotis Tzionas ()
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Vasiliki Mebelli: International Hellenic University
Maria Drakaki: International Hellenic University
Panagiotis Tzionas: International Hellenic University

SN Operations Research Forum, 2023, vol. 4, issue 2, 1-11

Abstract: 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.

Keywords: Mixed migration; Europe; Time series; Forecasting; Exponential smoothing; ARIMA; Asylum seekers (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s43069-023-00212-9

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