When Tomorrow Becomes Today: Self-Evolving Policies for Agentic Time-Series Forecasting

Problem

This work addresses the gap in time series forecasting systems that can adapt to evolving mechanisms. The authors highlight the necessity for models that not only predict future values but also adjust their strategies based on feedback from realized outcomes. This is particularly relevant in dynamic environments where traditional static models may fail to maintain accuracy over time. The paper is a preprint and has not yet undergone peer review.

Method

The core technical contribution is the introduction of TimEvolve, a frozen-backbone time series agent designed to implement a self-evolving policy framework. The architecture employs a novel algorithm characterized by a three-step protocol: predict, reveal, and update. This protocol facilitates the application of feedback to enhance the model's performance. The model processes data from eight Time-MMD domains, which are specifically chosen to evaluate the adaptability of the forecasting system. While the training compute requirements are not disclosed, the architecture is designed to convert realized outcomes into persistent updates that influence expert trust, agent path selection, and intervention strength.

Results

TimEvolve demonstrates superior performance metrics, achieving the best average rank in Mean Squared Error (MSE) and Mean Absolute Error (MAE) among fifteen evaluated methods. Specifically, it reports the lowest errors on both MSE and MAE across seven of the eight Time-MMD domains. However, the paper does not specify the baselines against which these results are compared, limiting the contextual understanding of the performance gains.

Limitations

The authors do not report any limitations in their study. However, the lack of specified baselines for comparison may hinder the assessment of the model's relative performance. Additionally, the absence of training compute details could limit reproducibility and practical application in resource-constrained environments.

Why it matters

The implications of this work are significant for downstream applications in time series forecasting, particularly in fields requiring real-time adaptability, such as finance, supply chain management, and climate modeling. By enabling models to evolve based on feedback, this research paves the way for more robust and resilient forecasting systems that can maintain accuracy in the face of changing data distributions.