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

MemCast introduces a memory-driven framework for time series forecasting that reformulates the task as experience-conditioned reasoning. The model organizes training data into a hierarchical memory structure, employing historical patterns, reasoning wisdom, and general laws to enhance prediction accuracy. Experimental results indicate that MemCast outperforms existing forecasting methods, making it a significant advancement for practitioners focused on improving the robustness and adaptability of time series models.