mlsanityData to 7 Oct 2026

Interpretable Classification of Time-Series Data using Efficient Enumerative Techniques

arXiv 1907.10265 ↗First posted 24 Jul 2019cs.LG stat.MLcited by 19

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Abstract (arXiv)

Cyber-physical system applications such as autonomous vehicles, wearable devices, and avionic systems generate a large volume of time-series data. Designers often look for tools to help classify and categorize the data. Traditional machine learning techniques for time-series data offer several solutions to solve these problems; however, the artifacts trained by these algorithms often lack interpretability. On the other hand, temporal logics, such as Signal Temporal Logic (STL) have been successfully used in the formal methods community as specifications of time-series behaviors. In this work, we propose a new technique to automatically learn temporal logic formulae that are able to cluster and classify real-valued time-series data. Previous work on learning STL formulas from data either assumes a formula-template to be given by the user, or assumes some special fragment of STL that enables exploring the formula structure in a systematic fashion. In our technique, we relax these assumptions, and provide a way to systematically explore the space of all STL formulas. As the space of all STL formulas is very large, and contains many semantically equivalent formulas, we suggest a technique to heuristically prune the space of formulas considered. Finally, we illustrate our technique on various case studies from the automotive, transportation and healthcare domain.

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 Jul 2019.

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Citing papers per year: 2021: 3; 2022: 3; 2023: 2; 2024: 5; 2025: 5; 2026: 1.

19 papers.

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  1. 2022Learning Signal Temporal Logic through Neural Network for Interpretable Classificationcited by 10
  2. 2021Classification of Time-Series Data Using Boosted Decision Treescited by 9
  3. 2024TLINet: Differentiable Neural Network Temporal Logic Inferencecited by 8
  4. 2022Differentiable Inference of Temporal Logic Formulascited by 5
  5. 2024Retrieval-Augmented Mining of Temporal Logic Specifications from Datacited by 5
  6. 2022Interactive Learning from Natural Language and Demonstrations using Signal Temporal Logiccited by 2
  7. 2023STL: Surprisingly Tricky Logic (for System Validation)cited by 2
  8. 2024Interpretable Imitation Learning via Generative Adversarial STL Inference and Controlcited by 2
  9. 2025Learning Probabilistic Temporal Logic Specifications for Stochastic Systemscited by 2
  10. 2021Mining Interpretable Spatio-temporal Logic Properties for Spatially Distributed Systemscited by 1
  11. 2023Joint Learning of Policy with Unknown Temporal Constraints for Safe Reinforcement Learningcited by 1
  12. 2024ECATS: Explainable-by-design concept-based anomaly detection for time seriescited by 1
7 more
  1. 2025Bridging Logic and Learning: Decoding Temporal Logic Embeddings via Transformerscited by 1
  2. 2021Time-Incremental Learning from Data Using Temporal Logicscited by 0
  3. 2024Learning Optimal Signal Temporal Logic Decision Trees for Classification: A Max-Flow MILP Formulationcited by 0
  4. 2025Automata Learning of Preferences over Temporal Logic Formulas from Pairwise Comparisonscited by 0
  5. 2025Towards Interpretable Concept Learning over Time Series via Temporal Logic Semanticscited by 0
  6. 2025Guided by Stars: Interpretable Concept Learning Over Time Series via Temporal Logic Semanticscited by 0
  7. 2026On Synthesis of Metric Interval Temporal Logicscited by 0