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
-
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.