PATRA: Pattern-Aware Alignment and Balanced Reasoning for Time Series Question Answering
#PATRA #Time Series Question Answering #Pattern Recognition #Machine Learning #AI Reasoning #Temporal Data #Large Language Models
📌 Key Takeaways
- PATRA addresses limitations in existing LLM-based approaches for time series question answering
- The model incorporates pattern-aware mechanisms to extract trend and seasonality patterns from time series data
- PATRA uses a task-aware balanced reward system to harmonize learning across tasks of varying difficulty
- Experiments demonstrate PATRA's superior performance across diverse time series question answering tasks
📖 Full Retelling
Researchers led by Junkai Lu and Peng Chen have introduced PATRA (Pattern-Aware Alignment and Balanced Reasoning), a new artificial intelligence model designed for time series question answering, in a paper submitted to arXiv on February 26, 2026, to address critical limitations in existing large language model approaches that fail to capture temporal patterns and balanced learning across tasks of varying complexity. The research team, which includes Xingjian Wu, Yang Shu, Chenjuan Guo, Christian S. Jensen, and Bin Yang, developed this innovative approach to overcome two major shortcomings in current AI systems when dealing with time series data. Existing models typically treat time series merely as text or images without recognizing essential patterns like trends and seasonalities, while also allowing simpler tasks to dominate the learning process when training on mixed-difficulty datasets, thereby hindering deep reasoning capabilities. To solve these problems, PATRA incorporates a pattern-aware mechanism that specifically extracts trend and seasonality patterns from time series to achieve deep alignment between data representation and question answering requirements. Additionally, the researchers designed a task-aware balanced reward system that harmonizes learning across tasks with different difficulty levels, encouraging the generation of coherent Chains of Thought essential for complex temporal reasoning. Extensive experiments demonstrate that PATRA significantly outperforms existing baseline models across diverse Time Series Question Answering tasks, showcasing superior cross-modal understanding and reasoning capabilities that could advance applications in finance, healthcare, climate science, and other domains reliant on temporal data analysis.
🏷️ Themes
Artificial Intelligence, Time Series Analysis, Machine Learning
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Original Source
--> Computer Science > Artificial Intelligence arXiv:2602.23161 [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 , 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 Time Series Question Answering tasks, demonstrating superior cross-modal understanding and reasoning capability. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2602.23161 [cs.AI] (or arXiv:2602.23161v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2602.23161 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Junkai Lu [ view email ] [v1] Thu, 26 Feb 2026 16:20:03 UTC (3,769 KB) Full-text links: Access Paper: View a PDF of the paper ...
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