Deep Learning for Temporal Stock Prediction: A Comparison
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Deep learning has emerging and numerous applications for most areas, including finance, physics and medical science, etc. The deep-based models achieve satisfying performance on those tasks. In this paper, we aim to provide a summary of deep learning-based temporal stock prediction. Specifically, we first categorize the models into three aspects, including CNN-based models, RNN-based models, and hybrid models, by combining CNN and RNN. Then, we detail the preliminary knowledge for deep-based models, including the components of CNN and RNN. Furthermore, we provide an in-depth review of those methods. Finally, we provide a perspective discussion on the stock prediction tasks for further research. We hope our method can be useful for future research and provide a brief introduction to beginners.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.033 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it