Multivariate Financial Time-Series Prediction With Certified Robustness
Bibliographic record
Abstract
The futures market's forecasts are significant to investors and policymakers, where the application of deep learning approaches to finance has received a great deal of attention. In this study, we propose a multivariate financial time-series forecasting method. Our model addresses the long- and short-term features, multimodal and non-stationarity nature of multivariate time-series by incorporating the improved deep neural networks and certified noise injection. Specifically, multimodal variational autoencoder is used to extract deep high-level features of multivariate time-series, Long- and Short- Term recurrent neural network is applied for multivariate time-series forecasting, and certified noise injection mechanism, inspired by differential privacy, is proposed to improve the robustness and accuracy of prediction. Extensive empirical results on real-world agricultural commodity futures price time series and relevant external data demonstrate that our model achieves better performance over that of several state-of-the-art baseline methods.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".