提出一种更高效准确的霍克斯过程变点检测方法
Conjugate Bayesian Two-step Change Point Detection for Hawkes Process
- 通过数据增广实现共轭贝叶斯推断，避免复杂数值计算
- 在合成与真实数据上均优于基线方法，计算效率显著提升
- 适合需要实时检测的金融、社交网络等时序场景
贝叶斯两步变点检测方法因简洁直观而广泛用于霍克斯过程。然而，点过程似然与先验之间的非共轭性导致现有方法多依赖非共轭推断，缺乏解析表达式，计算效率低，难以实现实时检测。为此，本文通过数据增广提出一种霍克斯过程的共轭贝叶斯两步变点检测方法，证明其更准确且高效。在合成数据和真实数据上的大量实验表明，该方法在有效性与效率上均优于基线方法。此外，我们还进行了消融实验，探究了多种超参数对方法鲁棒性的影响。代码已公开于 https://github.com/Aurora2050/CoBay-CPD。
原文摘要 · Abstract (English)
The Bayesian two-step change point detection method is popular for the Hawkes process due to its simplicity and intuitiveness. However, the non-conjugacy between the point process likelihood and the prior requires most existing Bayesian two-step change point detection methods to rely on non-conjugate inference methods. These methods lack analytical expressions, leading to low computational efficiency and impeding timely change point detection. To address this issue, this work employs data augmentation to propose a conjugate Bayesian two-step change point detection method for the Hawkes process, which proves to be more accurate and efficient. Extensive experiments on both synthetic and real data demonstrate the superior effectiveness and efficiency of our method compared to baseline methods. Additionally, we conduct ablation studies to explore the robustness of our method concerning various hyperparameters. Our code is publicly available at https://github.com/Aurora2050/CoBay-CPD.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。