用知识增强与数据高效机制，提升时间序列异常检测模型选择的准确率和速度
KDSelector: A Knowledge-Enhanced and Data-Efficient Model Selector Learning Framework for Time Series Anomaly Detection
- 融合历史数据中的知识，动态筛选重要样本，减少冗余计算
- 相比传统方法，训练速度更快，模型选择准确率显著提升
- 可直接替换现有系统模块，适合工业级时间序列分析场景
时间序列异常检测（TSAD）中，由于真实数据高度异构，不存在通用最优模型，因此模型选择成为关键问题。现有解决方案通过训练分类模型（尤其是神经网络）来预测每条序列应采用的TSAD模型，但其神经网络选择器未充分利用历史数据中的知识，且需遍历所有训练样本，导致准确率与训练速度受限。为此，我们提出KDSelector——一种知识增强、数据高效的神经网络型TSAD模型选择器学习框架。该框架包含三个核心组件：将可用知识融入选择器，动态剪枝不重要或冗余样本。我们构建了以KDSelector为内核的模型选择系统，验证其作为即插即用模块，能显著提升选择器的准确率与训练效率。演示视频见https://youtu.be/2uqupDWvTF0。
原文摘要 · Abstract (English)
Model selection has been raised as an essential problem in the area of time series anomaly detection (TSAD), because there is no single best TSAD model for the highly heterogeneous time series in real-world applications. However, despite the success of existing model selection solutions that train a classification model (especially neural network, NN) using historical data as a selector to predict the correct TSAD model for each series, the NN-based selector learning methods used by existing solutions do not make full use of the knowledge in the historical data and require iterating over all training samples, which limits the accuracy and training speed of the selector. To address these limitations, we propose KDSelector, a novel knowledge-enhanced and data-efficient framework for learning the NN-based TSAD model selector, of which three key components are specifically designed to integrate available knowledge into the selector and dynamically prune less important and redundant samples during the learning. We develop a TSAD model selection system with KDSelector as the internal, to demonstrate how users improve the accuracy and training speed of their selectors by using KDSelector as a plug-and-play module. Our demonstration video is hosted at https://youtu.be/2uqupDWvTF0.
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