Dynamic group fusion transformer for financial time series prediction: An ablation study
Bibliographic record
Abstract
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.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".