Guided by Stars: Interpretable Concept Learning Over Time Series via Temporal Logic Semantics

• URL: http://arxiv.org/abs/2511.04244v1
• Date: Thu, 06 Nov 2025 10:31:27 GMT
• Title: Guided by Stars: Interpretable Concept Learning Over Time Series via Temporal Logic Semantics
• Authors: Irene Ferfoglia, Simone Silvetti, Gaia Saveri, Laura Nenzi, Luca Bortolussi,
• Abstract summary: STELLE is a neuro-symbolic framework that unifies classification and explanation through direct embedding of trajectories into a space of temporal logic concepts.<n>Our model jointly optimises accuracy and interpretability, as each prediction is accompanied by the most relevant logical concepts that characterise it.<n>Experiments demonstrate that STELLE achieves competitive accuracy while providing logically faithful explanations, validated on diverse real-world benchmarks.
• Score: 3.071933369858584
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Time series classification is a task of paramount importance, as this kind of data often arises in safety-critical applications. However, it is typically tackled with black-box deep learning methods, making it hard for humans to understand the rationale behind their output. To take on this challenge, we propose a novel approach, STELLE (Signal Temporal logic Embedding for Logically-grounded Learning and Explanation), a neuro-symbolic framework that unifies classification and explanation through direct embedding of trajectories into a space of temporal logic concepts. By introducing a novel STL-inspired kernel that maps raw time series to their alignment with predefined STL formulae, our model jointly optimises accuracy and interpretability, as each prediction is accompanied by the most relevant logical concepts that characterise it. This yields (i) local explanations as human-readable STL conditions justifying individual predictions, and (ii) global explanations as class-characterising formulae. Experiments demonstrate that STELLE achieves competitive accuracy while providing logically faithful explanations, validated on diverse real-world benchmarks.

Related papers

• Do It for HER: First-Order Temporal Logic Reward Specification in Reinforcement Learning (Extended Version) [49.462399222747024]
  We propose a novel framework for the logical specification of non-Markovian rewards in Decision Processes (MDPs) with large state spaces.<n>Our approach leverages Linear Temporal Logic Modulo Theories over finite traces (LTLfMT)<n>We introduce a method based on reward machines and Hindsight Experience Replay (HER) to translate first-order logic specifications and address reward sparsity.
  arXiv  Detail & Related papers  (2026-02-05T22:11:28Z)
• Priors in Time: Missing Inductive Biases for Language Model Interpretability [58.07412640266836]
  We show that Sparse Autoencoders impose priors that assume independence of concepts across time, implying stationarity.<n>We introduce a new interpretability objective -- Temporal Feature Analysis -- which possesses a temporal inductive bias to decompose representations at a given time into two parts.<n>Our results underscore the need for inductive biases that match the data in designing robust interpretability tools.
  arXiv  Detail & Related papers  (2025-11-03T18:43:48Z)
• Towards Interpretable Concept Learning over Time Series via Temporal Logic Semantics [3.071933369858584]
  We propose a neuro-symbolic framework that unifies classification and explanation through direct embedding of trajectories.<n>By introducing a novel STL-inspired kernel that maps raw time series to their alignment with predefined STL formulae, our model jointly optimises for accuracy and interpretability.<n>Early results show competitive performance while offering high-quality logical justifications for model decisions.
  arXiv  Detail & Related papers  (2025-08-05T09:50:55Z)
• ECATS: Explainable-by-design concept-based anomaly detection for time series [0.5956301166481089]
  We propose ECATS, a concept-based neuro-symbolic architecture where concepts are represented as Signal Temporal Logic (STL) formulae. We show that our model is able to achieve great classification performance while ensuring local interpretability.
  arXiv  Detail & Related papers  (2024-05-17T08:12:53Z)
• TLINet: Differentiable Neural Network Temporal Logic Inference [10.36033062385604]
  This paper introduces TLINet, a neural-symbolic framework for learning STL formulas. In contrast to existing approaches, we introduce approximation methods for max operator designed specifically for temporal logic-based gradient techniques. Our framework not only learns the structure but also the parameters of STL formulas, allowing flexible combinations of operators and various logical structures.
  arXiv  Detail & Related papers  (2024-05-03T16:38:14Z)
• LOGICSEG: Parsing Visual Semantics with Neural Logic Learning and Reasoning [73.98142349171552]
  LOGICSEG is a holistic visual semantic that integrates neural inductive learning and logic reasoning with both rich data and symbolic knowledge. During fuzzy logic-based continuous relaxation, logical formulae are grounded onto data and neural computational graphs, hence enabling logic-induced network training. These designs together make LOGICSEG a general and compact neural-logic machine that is readily integrated into existing segmentation models.
  arXiv  Detail & Related papers  (2023-09-24T05:43:19Z)
• Generic Temporal Reasoning with Differential Analysis and Explanation [61.96034987217583]
  We introduce a novel task named TODAY that bridges the gap with temporal differential analysis. TODAY evaluates whether systems can correctly understand the effect of incremental changes. We show that TODAY's supervision style and explanation annotations can be used in joint learning.
  arXiv  Detail & Related papers  (2022-12-20T17:40:03Z)
• Linear Temporal Logic Modulo Theories over Finite Traces (Extended Version) [72.38188258853155]
  This paper studies Linear Temporal Logic over Finite Traces (LTLf) proposition letters are replaced with first-order formulas interpreted over arbitrary theories. The resulting logic, called Satisfiability Modulo Theories (LTLfMT), is semi-decidable.
  arXiv  Detail & Related papers  (2022-04-28T17:57:33Z)
• Multi-Agent Reinforcement Learning with Temporal Logic Specifications [65.79056365594654]
  We study the problem of learning to satisfy temporal logic specifications with a group of agents in an unknown environment. We develop the first multi-agent reinforcement learning technique for temporal logic specifications. We provide correctness and convergence guarantees for our main algorithm.
  arXiv  Detail & Related papers  (2021-02-01T01:13:03Z)
• Temporal Answer Set Programming [3.263632801414296]
  We present an overview on Temporal Logic Programming under the perspective of its application for Knowledge Representation and declarative problem solving. We focus on recent results of the non-monotonic formalism called Temporal Equilibrium Logic (TEL) that is defined for the full syntax. In a second part, we focus on practical aspects, defining a syntactic fragment called temporal logic programs closer to ASP, and explain how this has been exploited in the construction of the solver TELINGO.
  arXiv  Detail & Related papers  (2020-09-14T16:13:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.