Communication Papers of the 18th Conference on Computer Science and Intelligence Systems

DOI: http://dx.doi.org/10.15439/2023F3141

Citation: Communication Papers of the 18th Conference on Computer Science and Intelligence Systems, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 37, pages 301–308 (2023)

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Abstract. The study examines the potential of Google Trends data as an additional data source for forecasting EU migration to Germany. For that aim, candidate search querys with relation to migration intent are proposed. The resulting Google Trends Indices (GTI) are included in a machine learning regression model based on econometric and past migration data. It is shown that GTI predictors can moderately reduce the forecast error and enable a slight expansion of the forecast horizon. However, the presence of outliers emphasizes the need for continuous improvement in data quality to increase the robustness of the approach.

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