MemCast: Memory-Driven Time Series Forecasting with Experience-Conditioned Reasoning

MemCast introduces a novel time series forecasting framework that leverages large language models with hierarchical memory structures to improve prediction accuracy. The method organizes learned experiences into historical patterns, reasoning wisdom, and temporal laws, while incorporating dynamic confidence adaptation for continual learning without test set contamination.

MemCast represents an incremental but meaningful advancement in applying large language models to time series forecasting, a domain increasingly critical for financial markets, supply chain optimization, and resource planning. The framework's core innovation lies in formalizing how LLMs accumulate and leverage experience—traditionally, neural forecasters operate as stateless predictors without explicit knowledge retention mechanisms. By organizing learned patterns hierarchically and enabling reflective iteration, MemCast addresses a fundamental limitation in existing LLM-based forecasters.

The research builds on growing momentum in using language models for numerical prediction tasks. While LLMs weren't originally designed for quantitative forecasting, their reasoning capabilities and ability to process textual context have proven surprisingly effective. MemCast advances this trend by introducing mechanisms for experience accumulation and continual adaptation—features essential for real-world deployment where market regimes and data distributions shift over time.

For practitioners, the implications span multiple domains. Financial institutions exploring LLM-based forecasting for algorithmic trading or risk management could benefit from improved accuracy. Supply chain optimization and energy demand forecasting represent additional high-value applications. The dynamic confidence adaptation strategy is particularly noteworthy, as it enables models to evolve with new data without catastrophic forgetting or distribution leakage—a critical requirement for production systems.

Looking forward, the success of MemCast validates the broader research direction of combining symbolic reasoning with neural forecasting. Future work likely explores scaling these memory mechanisms to higher-dimensional financial data and integrating multi-modal signals, which could reshape how institutions approach quantitative prediction.

• →MemCast reformulates time series forecasting as experience-conditioned reasoning, organizing learned patterns into hierarchical memory structures.
• →The framework introduces dynamic confidence adaptation enabling continual model evolution without test set distribution leakage.
• →Experimental results demonstrate consistent improvements over existing LLM-based and traditional forecasting methods across multiple datasets.
• →The approach combines historical patterns, reasoning trajectories, and temporal laws to guide inference and improve prediction accuracy.
• →Code availability supports reproducibility and potential adoption in research and production forecasting pipelines.