Time series forecasts improve by learning from past outcomes
When Tomorrow Becomes Today: Self-Evolving Policies for Agentic Time-Series Forecasting
Machine LearningArtificial Intelligence
Summary
Predicting future trends can be tricky when the system itself changes over time, making some methods better at certain moments. The authors introduce TimEvolve, a new forecasting system that learns from actual past results to improve future predictions and decides which forecasting methods to trust based on experience. Unlike earlier approaches, TimEvolve adapts its strategy continuously by using feedback from real outcomes to update how it combines models and interventions. Tests show it outperforms many existing methods across several scenarios where the underlying systems evolve.
What this means in practice
- •For financial analysts: Enhance investment decision tools by adapting forecasts to evolving market dynamics using outcome-driven model trust updates.
- •For climate modelers: Improve accuracy of climate predictions by evolving forecasting policies based on feedback from previous observed conditions.
Authors
Yifan Hu, Xilin Dai, Zhiyuan Qu, Yiding Liu, Zewei Dong, Jiang-ming Yang, Qiang Xu
Abstract
Agentic time series forecasting concerns systems whose underlying mechanisms evolve, making the relative effectiveness of numerical models, reasoning strategies, and intervention rules inherently time-varying. Consequently, a time series agent must adapt the forecasts it produces and the orchestration policy that determines which components to trust and how to coordinate them. The deployment process naturally provides supervision for this adaptation as forecast horizons elapse and realized targets reveal the effectiveness of earlier decisions. Committing all numerical expert forecasts and candidate agent paths before target observation allows each realized outcome to evaluate the entire alternative set, providing delayed feedback without additional annotation. However, existing time series agents primarily incorporate prior experience through forecast refinement, reflection, or retrieval, without systematically converting realized outcomes into persistent updates to the joint orchestration policy governing later origins. To exploit this delayed feedback systematically, we introduce TimEvolve, a frozen-backbone time series agent that converts each realized outcome into persistent joint updates of expert trust, agent path selection, and intervention strength. A temporally ordered predict, reveal, and update protocol applies this feedback to subsequent forecasts. Experiments across eight Time-MMD domains show that TimEvolve achieves the best average MSE and MAE ranks among fifteen methods and the lowest errors on both metrics in seven domains. These results demonstrate the value of learning forecasting policies from the futures encountered during deployment.