arXivPaperNeeds Review

ConceptTS: LLM-Guided Concept Bottlenecks for Interpretable Multivariate Time-Series Forecasting

ConceptTS turns LLM-proposed concepts into executable labels and interpretable forecasting bottlenecks, enabling concept-level inspection and intervention.

arXiv

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Source Summary

**ConceptTS** asks an LLM to propose task-relevant concepts and executable labeling rules, avoiding manual concept annotation. It organizes predictions through **3 bottlenecks** covering history, local forecast intervals, and the full horizon before a shared decoder produces the forecast.

Practical Implication

For data agents, the useful pattern is to convert language-model domain knowledge into inspectable supervision rather than letting the LLM make the final prediction. Named activations can support debugging and direct concept-level interventions.

Agent-Ready Context

**ConceptTS** asks an LLM to propose task-relevant concepts and executable labeling rules, avoiding manual concept annotation. It organizes predictions through **3 bottlenecks** covering history, local forecast intervals, and the full horizon before a shared decoder produces the forecast.

For data agents, the useful pattern is to convert language-model domain knowledge into inspectable supervision rather than letting the LLM make the final prediction. Named activations can support debugging and direct concept-level interventions.

Evaluation is limited to the **Beijing Multi-Site Air Quality dataset**, where accuracy was described only as competitive with black-box baselines. The material does not quantify accuracy, labeling-rule errors, or transfer to other time-series domains.

Context Map

contextdata#context-engineering#tool-use

Uncertainty

Evaluation is limited to the **Beijing Multi-Site Air Quality dataset**, where accuracy was described only as competitive with black-box baselines. The material does not quantify accuracy, labeling-rule errors, or transfer to other time-series domains.