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[2509.24803] TimeOmni-1: Incentivizing Complex Reasoning with Time Series in Large Language Models
Summary
The paper introduces TimeOmni-1, a model designed to enhance complex reasoning with time series data in large language models, addressing limitations in current datasets and methodologies.
Why It Matters
This research is significant as it tackles the challenge of genuine reasoning in time series analysis, which is crucial for advancing AI applications in various fields such as finance, healthcare, and environmental monitoring. By providing a comprehensive reasoning suite and a new model, it opens avenues for improved decision-making and forecasting.
Key Takeaways
- TimeOmni-1 is the first unified reasoning model for time series data.
- The Time Series Reasoning Suite (TSR-Suite) formalizes four atomic tasks for effective reasoning.
- The model demonstrates significant improvements in causality discovery and forecasting accuracy.
- Over 23,000 samples in TSR-Suite enhance the training of time series reasoning models.
- TimeOmni-1's multi-stage training approach integrates diverse task scenarios and optimizations.
Computer Science > Artificial Intelligence arXiv:2509.24803 (cs) [Submitted on 29 Sep 2025 (v1), last revised 18 Feb 2026 (this version, v2)] Title:TimeOmni-1: Incentivizing Complex Reasoning with Time Series in Large Language Models Authors:Tong Guan, Zijie Meng, Dianqi Li, Shiyu Wang, Chao-Han Huck Yang, Qingsong Wen, Zuozhu Liu, Sabato Marco Siniscalchi, Ming Jin, Shirui Pan View a PDF of the paper titled TimeOmni-1: Incentivizing Complex Reasoning with Time Series in Large Language Models, by Tong Guan and 9 other authors View PDF HTML (experimental) Abstract:Recent advances in multimodal time series learning underscore a paradigm shift from analytics centered on basic patterns toward advanced time series understanding and reasoning. However, existing multimodal time series datasets mostly remain at the level of surface alignment and question answering, without reaching the depth of genuine reasoning. The absence of well-defined tasks that genuinely require time series reasoning, along with the scarcity of high-quality data, has limited progress in building practical time series reasoning models (TSRMs). To this end, we introduce Time Series Reasoning Suite (TSR-Suite), which formalizes four atomic tasks that span three fundamental capabilities for reasoning with time series: (1) perception, acquired through scenario understanding and causality discovery; (2) extrapolation, realized via event-aware forecasting; and (3) decision-making, developed through deliberation ov...
