An LSTM approach to Predict Migration based on Google Trends
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Being able to model and predict international migration as precisely as possible is crucial for policy making. Recently Google Trends data in addition to other economic and demographic data have been shown to improve the prediction quality of a gravity linear model for the one-year ahead predictions. In this work, we replace the linear model with a long short-term memory (LSTM) approach and compare it with two existing approaches: the linear gravity model and an artificial neural network (ANN) model. Our LSTM approach combined with Google Trends data outperforms both these models on various metrics in the task of predicting the one-year ahead incoming international migration to 34 OECD countries: the root mean square error has been divided by 5 on the test set and the mean average error by 4. This positive result demonstrates that machine learning techniques constitute a serious alternative over traditional approaches for studying migration mechanisms.
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