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[2602.23161] PATRA: Pattern-Aware Alignment and Balanced Reasoning for Time Series Question Answering
Summary
The paper presents PATRA, a novel model for Time Series Question Answering that enhances reasoning by incorporating pattern awareness and balanced learning across tasks of varying complexity.
Why It Matters
This research addresses significant limitations in current LLM-based approaches to time series analysis, which often overlook critical patterns. By improving alignment and reasoning capabilities, PATRA could enhance applications in fields like finance, healthcare, and climate science, where time series data is prevalent.
Key Takeaways
- PATRA introduces a pattern-aware mechanism to extract trends and seasonality from time series data.
- The model employs a balanced reward system to improve learning across tasks of varying difficulty.
- Experimental results show that PATRA outperforms existing models in Time Series Question Answering tasks.
- Enhanced reasoning capabilities can lead to better decision-making in various applications.
- The research highlights the importance of addressing the limitations of LLMs in handling complex time series data.
Computer Science > Artificial Intelligence arXiv:2602.23161 (cs) [Submitted on 26 Feb 2026] Title:PATRA: Pattern-Aware Alignment and Balanced Reasoning for Time Series Question Answering Authors:Junkai Lu, Peng Chen, Xingjian Wu, Yang Shu, Chenjuan Guo, Christian S. Jensen, Bin Yang View a PDF of the paper titled PATRA: Pattern-Aware Alignment and Balanced Reasoning for Time Series Question Answering, by Junkai Lu and Peng Chen and Xingjian Wu and Yang Shu and Chenjuan Guo and Christian S. Jensen and Bin Yang View PDF Abstract:Time series reasoning demands both the perception of complex dynamics and logical depth. However, existing LLM-based approaches exhibit two limitations: they often treat time series merely as text or images, failing to capture the patterns like trends and seasonalities needed to answer specific questions; and when trained on a mix of simple and complex tasks, simpler objectives often dominate the learning process, hindering the development of deep reasoning capabilities. To address these limitations, we propose the Pattern-Aware Alignment and Balanced Reasoning model (PATRA), introducing a pattern-aware mechanism that extracts trend and seasonality patterns from time series to achieve deep alignment. Furthermore, we design a task-aware balanced reward to harmonize learning across tasks of varying difficulty, incentivizing the generation of coherent Chains of Thought. Extensive experiments show that PATRA outperforms strong baselines across diverse Ti...