Macroeconomic Forecasting Using Transformer-Based Time Series Models

Massod, Dina; Farhan, Hameed

doi:10.25673/123732

Proceedings of International Conference on Applied Innovation in IT · 2026/03/31 · Vol. 14 · Issue 1 · pp. 1295–1301

Macroeconomic Forecasting Using Transformer-Based Time Series Models

Dina Fallah Massod, Hameed H. Khalaf and Mohsen Aued Farhan

📄 Download PDF DOI: 10.25673/123732

Abstract

Macroeconomic forecasting is essential for policy formulation, risk evaluation, and investment strategy development. However, conventional econometric models like ARIMA and VAR frequently encounter difficulties with high-dimensional data, structural breaks, and long-term forecasting issues. In this research, we create and test transformer-based architectures-Autoformer, ETSformer, iTransformer, and Informer-for multi-horizon macroeconomic forecasting of important indicators such as GDP growth, CPI inflation, the unemployment rate, and policy interest rates. The models are compared to classical and deep learning baselines like ARIMA, VAR, Prophet, LSTM, and GRU using publicly available datasets from FRED, OECD, and the World Bank. Results from rolling-origin evaluation show that transformer-based models always do better than baselines over both short- and long-term periods, lowering RMSE and MAE by as much as 20-35%. Also, using attention heatmaps for interpretability analysis shows how important policy rates and commodity prices are during times of inflation. These results show that transformers are good for making real-world macroeconomic decisions because they are both accurate and clear.

Keywords

Macroeconomic Forecasting Transformers Time Series Multi-Horizon Prediction Interpretability Policy Analytics.

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