用主动学习+深度高斯过程检测非平稳时间序列中的多个突变点
Active Learning for Multiple Change Point Detection in Non-stationary Time Series with Deep Gaussian Processes
- 结合谱分析与主动学习，智能选择采样点提升效率
- 在模拟和真实数据上检测准确率与采样效率均优于现有方法
- 适合处理具有复杂变化模式的非平稳时间序列分析
非平稳时间序列中的多突变点（MCP）检测因潜在模式多样而极具挑战。为此，本文提出一种新算法，将主动学习（AL）与深度高斯过程（DGPs）相结合，实现鲁棒的MCP检测。方法利用谱分析识别潜在变化点，并通过主动学习策略选择关键采样点以提高效率。通过融合DGPs的建模灵活性与谱方法的变点识别能力，该方法能适应多种谱变化行为，有效定位多个突变点。在模拟数据和真实数据上的实验表明，本方法在检测准确率和采样效率方面均优于现有技术。
原文摘要 · Abstract (English)
Multiple change point (MCP) detection in non-stationary time series is challenging due to the variety of underlying patterns. To address these challenges, we propose a novel algorithm that integrates Active Learning (AL) with Deep Gaussian Processes (DGPs) for robust MCP detection. Our method leverages spectral analysis to identify potential changes and employs AL to strategically select new sampling points for improved efficiency. By incorporating the modeling flexibility of DGPs with the change-identification capabilities of spectral methods, our approach adapts to diverse spectral change behaviors and effectively localizes multiple change points. Experiments on both simulated and real-world data demonstrate that our method outperforms existing techniques in terms of detection accuracy and sampling efficiency for non-stationary time series.
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