Time-RA reformulates time series anomaly detection as a reasoning-intensive generative task and provides the RATs40K multimodal benchmark to evaluate and improve LLM-based diagnosis.
Towards time series reasoning with llms
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
representative citing papers
Presents TS-Skill benchmark and SKEvol construction framework to diagnose three composable analytical skills in time-series QA across LLMs and TSLMs.
FinSTaR reaches 78.9% average accuracy on the new FinTSR-Bench by using Compute-in-CoT for deterministic assessment tasks and Scenario-Aware CoT for stochastic prediction tasks.
citing papers explorer
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Time-RA: Towards Time Series Reasoning for Anomaly Diagnosis with LLM Feedback
Time-RA reformulates time series anomaly detection as a reasoning-intensive generative task and provides the RATs40K multimodal benchmark to evaluate and improve LLM-based diagnosis.
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TS-Skill: A Benchmark for Evaluating Analytical Skills in Time-Series Question Answering
Presents TS-Skill benchmark and SKEvol construction framework to diagnose three composable analytical skills in time-series QA across LLMs and TSLMs.
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FinSTaR: Towards Financial Reasoning with Time Series Reasoning Models
FinSTaR reaches 78.9% average accuracy on the new FinTSR-Bench by using Compute-in-CoT for deterministic assessment tasks and Scenario-Aware CoT for stochastic prediction tasks.