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[2602.13802] Cast-R1: Learning Tool-Augmented Sequential Decision Policies for Time Series Forecasting
Summary
The paper presents Cast-R1, a novel framework for time series forecasting that reformulates the problem as a sequential decision-making task, enhancing predictive accuracy through iterative reasoning and tool interaction.
Why It Matters
This research addresses the limitations of traditional model-centric forecasting methods, which often fail in complex environments. By introducing a memory-based state management and a tool-augmented workflow, Cast-R1 aims to improve long-term forecasting capabilities, making it relevant for industries reliant on accurate time series predictions.
Key Takeaways
- Cast-R1 reformulates time series forecasting as a sequential decision-making problem.
- The framework utilizes a memory-based state management mechanism for better context retention.
- It employs a two-stage learning strategy combining supervised fine-tuning and reinforcement learning.
- Extensive experiments show Cast-R1's effectiveness on real-world datasets.
- The approach aims to advance agentic paradigms in time series modeling.
Computer Science > Machine Learning arXiv:2602.13802 (cs) [Submitted on 14 Feb 2026] Title:Cast-R1: Learning Tool-Augmented Sequential Decision Policies for Time Series Forecasting Authors:Xiaoyu Tao, Mingyue Cheng, Chuang Jiang, Tian Gao, Huanjian Zhang, Yaguo Liu View a PDF of the paper titled Cast-R1: Learning Tool-Augmented Sequential Decision Policies for Time Series Forecasting, by Xiaoyu Tao and 5 other authors View PDF HTML (experimental) Abstract:Time series forecasting has long been dominated by model-centric approaches that formulate prediction as a single-pass mapping from historical observations to future values. Despite recent progress, such formulations often struggle in complex and evolving settings, largely because most forecasting models lack the ability to autonomously acquire informative evidence, reason about potential future changes, or revise predictions through iterative decision processes. In this work, we propose Cast-R1, a learned time series forecasting framework that reformulates forecasting as a sequential decision-making problem. Cast-R1 introduces a memory-based state management mechanism that maintains decision-relevant information across interaction steps, enabling the accumulation of contextual evidence to support long-horizon reasoning. Building on this formulation, forecasting is carried out through a tool-augmented agentic workflow, in which the agent autonomously interacts with a modular toolkit to extract statistical features, invoke...
