Gross Domestic Product Forecasting Using Deep Learning Models with a Phase-Adaptive Attention Mechanism

Abstract

Forecasting GDP is a highly practical task in macroeconomics, especially in the context of rapidly changing economic environments caused by both economic and non-economic factors. This study proposes a deep learning model that integrates Long Short-Term Memory (LSTM) networks with a phase-adaptive attention mechanism (PAA-LSTM model) to improve forecasting accuracy. The attention mechanism is flexibly adjusted according to different phases of the economic cycle—recession, recovery, expansion, and stagnation—allowing the model to better capture temporal dynamics compared to traditional static attention approaches. The model is evaluated using GDP data from six countries representing three groups of economies: developed, emerging, and developing. The experimental results show that the proposed model achieves superior accuracy in countries with strong cyclical structures and high volatility. In more stable economies, such as the United States and Canada, PAA-LSTM remains competitive; however, its margin over simpler models is narrower, suggesting that the benefits of added complexity may vary depending on economic structure. These findings underscore the value of incorporating economic cycle phase information into deep learning models for macroeconomic forecasting and suggest a promising direction for selecting flexible forecasting architectures tailored to different country groups.