Time Series Reasoning via Process-Verifiable Thinking Data Synthesis and Scheduling for Tailored LLM Reasoning

Researchers introduce VeriTime, a framework that enhances large language models for time series analysis through synthetic data generation, intelligent data scheduling, and specialized reinforcement learning. The approach enables smaller models (3B-4B parameters) to match or exceed the reasoning capabilities of larger proprietary LLMs on time series tasks.

VeriTime addresses a critical gap in applying large language models to time series data, a ubiquitous challenge across finance, forecasting, and analytics. While LLMs have demonstrated strong reasoning abilities through chain-of-thought prompting and reinforcement learning, their application to temporal data has lagged due to lack of curated training data and inefficient learning strategies. The framework tackles this through three coordinated innovations: process-verifiable data synthesis creates high-quality multimodal training examples with explainable intermediate steps, data scheduling organizes samples by difficulty and task type to optimize learning progression, and tailored RL training extracts maximum value from annotated reasoning paths. The results carry significant implications for enterprise AI deployment. Smaller models achieving parity with larger proprietary systems reduce computational costs and dependency on expensive API-based solutions, democratizing advanced time series reasoning across organizations. This efficiency gain matters particularly for financial forecasting, demand prediction, and anomaly detection where reasoning transparency is increasingly required for regulatory compliance and stakeholder trust. The research demonstrates that training data quality and curriculum design can overcome raw parameter advantages, suggesting the field is moving toward more intelligent rather than larger models. For developers and AI teams, this indicates emerging tools may soon enable competitive time series analysis without requiring cutting-edge hardware or proprietary model access, potentially disrupting the current market for specialized time series software.

• →VeriTime enables 3B-4B parameter models to match reasoning performance of much larger proprietary LLMs on time series tasks
• →Process-verifiable data synthesis creates transparent, annotated reasoning paths that improve model interpretability and performance
• →Intelligent data scheduling by difficulty and task taxonomy significantly improves training efficiency compared to random sampling
• →Framework combines synthetic data generation, curriculum learning, and multi-objective reward RL in an integrated approach
• →Results suggest model efficiency and training data quality matter more than parameter count for time series reasoning