MOMEMTO: Patch-based Memory Gate Model in Time Series Foundation Model
It addresses the problem of anomaly detection in time series for domains requiring multi-dataset adaptability, though it is incremental as it builds on existing memory and foundation model approaches.
The paper tackles over-generalization in reconstruction-based time series anomaly detection models by proposing MOMEMTO, a time series foundation model enhanced with a patch-based memory module, which achieves higher AUC and VUS scores on 23 benchmark datasets compared to baselines.
Recently reconstruction-based deep models have been widely used for time series anomaly detection, but as their capacity and representation capability increase, these models tend to over-generalize, often reconstructing unseen anomalies accurately. Prior works have attempted to mitigate this by incorporating a memory architecture that stores prototypes of normal patterns. Nevertheless, these approaches suffer from high training costs and have yet to be effectively integrated with time series foundation models (TFMs). To address these challenges, we propose \textbf{MOMEMTO}, a TFM for anomaly detection, enhanced with a patch-based memory module to mitigate over-generalization. The memory module is designed to capture representative normal patterns from multiple domains and enables a single model to be jointly fine-tuned across multiple datasets through a multi-domain training strategy. MOMEMTO initializes memory items with latent representations from a pre-trained encoder, organizes them into patch-level units, and updates them via an attention mechanism. We evaluate our method using 23 univariate benchmark datasets. Experimental results demonstrate that MOMEMTO, as a single model, achieves higher scores on AUC and VUS metrics compared to baseline methods, and further enhances the performance of its backbone TFM, particularly in few-shot learning scenarios.