TSOrchestra: Time Series Agentic Orchestration Framework towards Dynamic and Faithful Forecasting

Time series are inherently dynamic and diverse, meaning a single foundation model can seldom maintain universal dominance across varying regimes. Consequently, the frontier of forecasting has shifted from seeking a single optimal architecture to dynamic model orchestration. While Large Language Models (LLMs) offer powerful reasoning capabilities, their direct application to forecasting remains ineffective. We propose TSOrchestra, a novel framework that bridges mathematical execution with agentic reasoning by repositioning the LLM as a metacognitive auditor. While the underlying numerical weights are determined by a continuous optimization solver, the LLM auditor actively steers the optimization trajectory in real-time through iterative hypothesis testing and dynamic metric routing. To instill domain-specific heuristics, we introduce an R1-style reinforcement learning pipeline. The agent's policy is regularized by structural faithfulness rewards, ensuring its audit trails are strictly anchored to empirical, decomposition-based temporal dynamics. Validated on the GIFT-Eval benchmark across 23 datasets and 97 settings, TSOrchestra consistently outperforms all leading time-series foundation models on both CRPS and MASE metrics, establishing new state-of-the-art results.