MemTime: Hippocampus-Inspired Agents for Adaptive Time Series Forecasting with Evolving Memory

Pre-trained time-series foundation models have recently achieved remarkable zero-shot forecasting performance across diverse domains, providing a powerful paradigm for time-series prediction. However, most TSFMs remain fixed after pretraining, limiting their ability to incorporate new experiences as data distributions evolve. Inspired by hippocampal memory mechanisms, which enable rapid experience encoding, selective recall, and long-term consolidation, we investigate how dynamic memory processes can endow time-series foundation models with continual adaptation capabilities. Realizing such adaptation requires a mechanism that can effectively identify, incorporate, and evolve experiences throughout streaming forecasting. Motivated by these observations, we introduce MemTime, an autonomous self-evolving time-series agent framework that enables foundation models to adapt through dynamic memory augmentation without modifying their underlying parameters. Specifically, MemTime introduces a self-evolving memory mechanism that enables foundation models to acquire, utilize, and consolidate deployment-time experiences. It incorporates a reinforcement learning-based Cognitive Sentinel, which captures environmental changes and adaptively determines when memory retrieval is required. The Mental Retracer transforms retrieved experiences into memory-based correction signals, which adjust the forecasting process without updating the model parameters. To support long-term adaptation, a memory metabolism mechanism periodically consolidates the memory buffer by removing outdated experiences while preserving valuable knowledge. Extensive experiments on multiple non-stationary forecasting benchmarks demonstrate that MemTime consistently outperforms state-of-the-art adaptation and external-memory baselines, achieving improvements of up to 26.03% while maintaining cross-domain generalization. The code is publicly available at https://anonymous.4open.science/r/MemTime-050A/.