Gross Domestic Product Forecasting Using Deep Learning Models with a Phase-Adaptive Attention Mechanism
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".