Adaptive Time Series Reasoning via Segment Selection
arXiv:2602.18645v1 Announce Type: new Abstract: Time series reasoning tasks often start with a natural language question and require targeted analysis of a time series. Evidence may span the full series or appear in a few short intervals, so the model must decide what to inspect. Most existing approaches encode the entire time series into a fixed representation before inference, regardless of whether or not the entire sequence is relevant. We introduce ARTIST, which formulates time-series reasoning as a sequential decision problem. ARTIST interleaves reasoning with adaptive temporal segment selection. It adopts a controller-reasoner architecture and uses reinforcement learning to train the controller role to select informative segments and the reasoner role to generate segment-conditioned reasoning traces and final answers. During inference, the model actively acquires task-relevant information instead of relying on a static summary of the full sequence. We use a novel hierarchical po
arXiv:2602.18645v1 Announce Type: new Abstract: Time series reasoning tasks often start with a natural language question and require targeted analysis of a time series. Evidence may span the full series or appear in a few short intervals, so the model must decide what to inspect. Most existing approaches encode the entire time series into a fixed representation before inference, regardless of whether or not the entire sequence is relevant. We introduce ARTIST, which formulates time-series reasoning as a sequential decision problem. ARTIST interleaves reasoning with adaptive temporal segment selection. It adopts a controller-reasoner architecture and uses reinforcement learning to train the controller role to select informative segments and the reasoner role to generate segment-conditioned reasoning traces and final answers. During inference, the model actively acquires task-relevant information instead of relying on a static summary of the full sequence. We use a novel hierarchical policy optimization approach for post-training that allows the model to excel in both segment selection and question-answering behavior. We evaluate ARTIST on six time-series reasoning benchmarks and compare it with large language models, vision-language models, and prior time-series reasoning systems. ARTIST improves average accuracy by 6.46 absolute percentage points over the strongest baseline. The largest gains appear on rare event localization and multi-segment reasoning tasks. Supervised fine-tuning improves performance, and reinforcement learning provides additional gains by optimizing question-adaptive segment selection. These results show that selective data use drives effective time-series reasoning.
Executive Summary
This article introduces ARTIST, a novel time-series reasoning framework that employs a controller-reasoner architecture and reinforcement learning to selectively acquire task-relevant information. By interleaving reasoning with adaptive temporal segment selection, ARTIST outperforms existing methods, achieving a 6.46% absolute improvement in average accuracy. The model excels particularly in rare event localization and multi-segment reasoning tasks, demonstrating the effectiveness of selective data use in time-series reasoning. The authors also explore the benefits of post-training hierarchical policy optimization and supervised fine-tuning. While the results are promising, further investigation is necessary to fully understand the model's generalizability and potential applications.
Key Points
- ▸ ARTIST employs a controller-reasoner architecture and reinforcement learning for selective data acquisition
- ▸ The model excels in rare event localization and multi-segment reasoning tasks
- ▸ Hierarchical policy optimization and supervised fine-tuning improve performance
Merits
Improved Accuracy
ARTIST achieves a 6.46% absolute improvement in average accuracy over the strongest baseline, demonstrating its effectiveness in time-series reasoning tasks.
Selective Data Use
The model's ability to selectively acquire task-relevant information enables efficient and effective processing of time-series data.
Flexibility and Adaptability
ARTIST's controller-reasoner architecture and reinforcement learning framework allow the model to adapt to diverse time-series reasoning tasks and scenarios.
Demerits
Limited Generalizability
Further investigation is necessary to fully understand the model's generalizability to diverse time-series data and reasoning tasks.
Computational Resource Intensity
The use of reinforcement learning and hierarchical policy optimization may require significant computational resources, potentially limiting the model's deployment in resource-constrained environments.
Interpretability and Explainability
The complex architecture and learning mechanisms employed by ARTIST may compromise the model's interpretability and explainability, making it challenging to understand and trust the results.
Expert Commentary
The introduction of ARTIST represents a significant step forward in time-series analysis and reasoning. By leveraging reinforcement learning and hierarchical policy optimization, the authors have developed a model that can selectively acquire task-relevant information and adapt to diverse time-series reasoning tasks. While further investigation is necessary to fully understand the model's generalizability and potential applications, the results presented in this article are promising. As the field continues to evolve, it is likely that techniques like ARTIST will play an increasingly important role in a wide range of applications, from finance and healthcare to environmental monitoring and climate modeling.
Recommendations
- ✓ Future research should focus on investigating the model's generalizability to diverse time-series data and reasoning tasks.
- ✓ The development of more interpretable and explainable architectures for time-series analysis and reasoning is essential for building trust in these technologies.
