Interpreting time series forecasting models using model classreliance
Bibliographic record
Abstract
The widespread use of complex machine learning models in various data science tasks has made it difficult to understand how these models make decisions, leading to a growing interest in AI interpretability. This is especially true for time series data, where applying standard interpretability methods introduces unique challenges that have not been fully explored yet. This paper compares the model class reliance (MR) scores with existing state-of-the-art interpretability measures for time series forecasting models and provides the first application of MR for probabilistic time series forecasting. We employ several post-hoc interpretability techniques on different time series forecasting models and datasets, comparing MR scores against the benchmark SHAP scores. Additionally, we apply MR to deep probabilistic forecasting models, evaluating the outcomes against those achieved through feature removal and model retraining. Our findings indicate that MR scores are consistent with SHAP scores, particularly for important features, and can accurately prioritize feature importance in deep probabilistic forecasting models. Our work demonstrates that MR-based interpretations not only align with SHAP but also MR method stands out for its ease of implementation and efficiency, making it a viable option for interpreting complex time series forecasting models.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".