An LSTM approach to Forecast Migration using Google Trends
This work addresses forecasting migration for policymaking, showing incremental improvement by applying a known machine learning method to an existing data setup.
The paper tackled forecasting international migration by replacing a linear gravity model with an LSTM approach using Google Trends data, resulting in a reduction of RMSE and MAE by factors of 5 and 4 on the test set compared to existing models.
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.