Temporal Dependencies in Feature Importance for Time Series Predictions.
Bibliographic record
Abstract
Explanation methods applied to sequential models for multivariate time series prediction are receiving more attention in machine learning literature. While current methods perform well at providing instance-wise explanations, they struggle to efficiently and accurately make attributions over long periods of time and with complex feature interactions. We propose WinIT, a framework for evaluating feature importance in time series prediction settings by quantifying the shift in predictive distribution over multiple instances in a windowed setting. Comprehensive empirical evidence shows our method improves on the previous state-of-the-art, FIT, by capturing temporal dependencies in feature importance. We also demonstrate how the solution improves the appropriate attribution of features within time steps, which existing interpretability methods often fail to do. We compare with baselines on simulated and real-world clinical data. WinIT achieves 2.47x better performance than FIT and other feature importance methods on real-world clinical MIMIC-mortality task. The code for this work is available at this https URL.
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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 | 0.005 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".