A Proposal of Transfer Learning for Monthly Macroeconomic Time Series Forecast

Abstract

Transfer learning has not been widely explored with time series. However, it could boost the application and performance of deep learning models for predicting macroeconomic time series with few observations, like monthly variables. In this study, we propose to generate a forecast of five macroeconomic variables using deep learning and transfer learning. The models were evaluated with cross-validation on a rolling basis and the metric MAPE. According to the results, deep learning models with transfer learning tend to perform better than deep learning models without transfer learning and other machine learning models. The difference between statistical models and transfer learning models tends to be small. Although, in some series, the statistical models had a slight advantage in terms of the performance metric, the results are promising for the application of transfer learning to macroeconomic time series.

Keywords

• macroeconomic forecast
• deep learning
• transfer learning
• benchmark