  Being able to model and forecast international migration as precisely as
possible is crucial for policymaking. Recently Google Trends data in addition
to other economic and demographic data have been shown to improve the
forecasting quality of a gravity linear model for the one-year ahead
forecasting. 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 forecasting the one-year ahead incoming international
migration to 35 Organization for Economic Co-operation and Development (OECD)
countries: for example the root mean square error (RMSE) and the mean average
error (MAE) have been divided by 5 and 4 on the test set. This positive result
demonstrates that machine learning techniques constitute a serious alternative
over traditional approaches for studying migration mechanisms.
