Dynamic group fusion transformer for financial time series prediction: An ablation study

Forecasting financial time series is particularly challenging since market data is complicated and non-stationary, and it is necessary to identify both short-term momentum and long-term structural patterns. This work develops the Dynamic Group Fusion Enhanced Transformer (DGFET), a new approach that integrates adaptive feature group fusion and selective information processing. The suggested DGFET architecture has Group-FiLM adapters that use Dynamic Group Fusion techniques for adaptive feature transformation to manage market, fundamental, technical, and sentiment feature groups. We assess the model using four unique labeling strategies: short-horizon momentum (binary/ternary) and triple-barrier (binary/ternary), which represent various temporal horizons and forecasting methods. Our ablation analysis, conducted on a comprehensive EUR/USD dataset from 2010 to 2023 with 88 features, demonstrates that the proposed method consistently outperforms baseline LSTM and standard transformer models across all prediction objectives. The improved architecture has a higher overall performance, with an F1-macro score of 0.5356 and a ROC-AUC of 0.6612. It also works very well for short-horizon momentum binary classification (F1: 0.7219, ROC-AUC: 0.8105). The results show that adaptive feature fusion works better than traditional designs when combined with dynamic group selection. The best configurations depend on the specific prediction job. Our results underscore the imperative of task-specific architectural design in financial machine learning applications, especially for methodologies necessitating varied temporal horizons and prediction granularities.