Cast-R1: Learning Tool-Augmented Sequential Decision Policies for Time Series Forecasting

TL;DR

Cast-R1 is proposed, a learned time series forecasting framework that reformulates forecasting as a sequential decision-making problem, and 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.

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 lightweight forecasting models for decision support, perform reasoning-based prediction, and iteratively refine forecasts through self-reflection. To train Cast-R1, we adopt a two-stage learning strategy that combines supervised fine-tuning with multi-turn reinforcement learning, together with a curriculum learning scheme that progressively increases task difficulty to improve policy learning. Extensive experiments on multiple real-world time series datasets demonstrate the effectiveness of Cast-R1. We hope this work provides a practical step towards further exploration of agentic paradigms for time series modeling. Our code is available at https://github.com/Xiaoyu-Tao/Cast-R1-TS.

Figures (6)

• Figure 1: Overview of the Cast-R1 framework for agentic time series forecasting.
• Figure 2: Memory-based state management mechanism in Cast-R1 for multi-step forecasting decisions.
• Figure 3: Performance comparison of ablation studies and analysis. Impact of (a) dynamic memory, (b) training strategies (SFT vs. RL), and (c) curriculum learning. (d) Scalability analysis across different backbone model sizes.
• Figure 4: Visualization of training curves. (a) SFT initialization accelerates convergence compared to Pure RL. (b) Larger backbone models consistently yield higher reward scores.
• Figure 5: Qualitative case study. The agent leverages statistical diagnosis, adaptive model routing to Chronos-2, and self-reflection to mitigate volatility.