用分块记忆模块防止时间序列模型过度拟合，提升异常检测精度。
Patch-based Memory Gate Model in Time Series Foundation Model
- 设计分块记忆模块，存储多领域正常模式的代表性特征。
- 在23个单变量数据集上，AUC和VUS均优于基线方法。
- 适合需要少样本学习的工业异常检测场景。
近期基于重构的深度模型广泛用于时间序列异常检测，但随着模型容量与泛化能力提升，这些模型常过度泛化，能准确重构未见异常。现有方法尝试通过记忆架构存储正常模式原型来缓解此问题，但训练成本高，且尚未有效融入时间序列基础模型（TSFM）。为此，我们提出MOMEMTO，一种增强型TSFM变体，引入分块记忆模块以抑制过拟合。该模块从多个领域捕获代表性正常模式，支持跨领域联合微调。MOMEMTO利用预训练编码器的隐向量初始化记忆项，按块级单位组织，并通过注意力机制更新。我们在23个单变量基准数据集上评估该方法。实验表明，作为单一模型，MOMEMTO在AUC和VUS指标上均优于基线方法，且显著提升了其骨干TSFM的性能，尤其在少样本学习场景下表现突出。
原文摘要 · Abstract (English)
Recently reconstruction-based deep models have been widely used for time series anomaly detection, but as their capacity and generalization 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 (TSFMs). To address these challenges, we propose MOMEMTO, an improved TSFM variant 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 these domains 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 TSFM, particularly in few-shot learning scenarios.
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