The Failure-Driven Evolution Loop

TimeEvo introduces a self-improving framework designed to overcome the limitations of static LLM-based agents in time series forecasting. Instead of relying on fixed prompts or static architectures, the system employs a 'failure-driven' approach. When the agent produces an inaccurate forecast, the framework triggers an analysis phase that diagnoses the specific reasoning error—such as misinterpreting trend patterns, ignoring seasonality, or failing to account for external covariates. This diagnostic data is then used to update the agent's internal logic or prompt strategy, effectively creating a feedback loop that evolves the agent's performance over time.

Moving Beyond Static Forecasting

Traditional time series agents often struggle with the dynamic nature of temporal data, frequently defaulting to generic patterns that fail to capture domain-specific nuances. TimeEvo addresses this by treating the forecasting process as an iterative optimization task. By systematically documenting 'failures' (instances where the model's prediction deviates significantly from ground truth), the agent builds a knowledge base of edge cases. This allows the system to adjust its decision-making process dynamically, ensuring that the agent becomes more robust to data shifts and complex temporal dependencies without requiring full model retraining. The framework demonstrates that self-reflection on past errors is a more efficient path to accuracy than simply increasing model scale.