Conformal Prediction for Signal Temporal Logic Inference
This addresses the need for reliable and interpretable rule extraction in time-series analysis, though it is incremental as it builds on existing conformal prediction and STL methods.
The paper tackled the problem of lacking formal confidence guarantees in Signal Temporal Logic (STL) inference from time-series data by introducing an end-to-end differentiable conformal prediction framework, resulting in reduced uncertainty and improved accuracy over state-of-the-art baselines.
Signal Temporal Logic (STL) inference seeks to extract human-interpretable rules from time-series data, but existing methods lack formal confidence guarantees for the inferred rules. Conformal prediction (CP) is a technique that can provide statistical correctness guarantees, but is typically applied as a post-training wrapper without improving model learning. Instead, we introduce an end-to-end differentiable CP framework for STL inference that enhances both reliability and interpretability of the resulting formulas. We introduce a robustness-based nonconformity score, embed a smooth CP layer directly into training, and employ a new loss function that simultaneously optimizes inference accuracy and CP prediction sets with a single term. Following training, an exact CP procedure delivers statistical guarantees for the learned STL formulas. Experiments on benchmark time-series tasks show that our approach reduces uncertainty in predictions (i.e., it achieves high coverage while reducing prediction set size), and improves accuracy (i.e., the number of misclassifications when using a fixed threshold) over state-of-the-art baselines.