The study demonstrates deep learning models improve data classification accuracy in aviation, suggesting potential for future research trends forecasting.
Deep learning (DL) and machine learning (ML) models have been successfully applied across multiple domains, but generic architectures often underperform without domain-specific adaptation. This study presents A-BERT, a BERT-based model fine-tuned on a dataset of aviation and aircraft-related academic publications, enabling accurate classification into 14 thematic categories. The temporal evolution of publication counts in each category was then modeled using ARIMA to forecast future research trends in the aviation sector. As a proof of concept, A-BERT outperformed the baseline BERT in several key metrics, offering a reliable approach for large-scale, domain-specific literature classification. Forecast validation through walk-forward testing across multiple time windows yielded Root Mean Square Error (RMSE) values below 2% for all categories, confirming high predictive reliability within this controlled setting. While the framework demonstrates the potential of combining domain-specific text classification with validated time series forecasting, its extension to operational aviation datasets will require further adaptation and external validation.
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Lázaro et al. (2025) studied this question.
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