Conjugate Bayesian Two-step Change Point Detection for Hawkes Process
This work addresses a computational bottleneck for researchers and practitioners using Hawkes processes in fields like finance or social media, though it is incremental as it builds on existing Bayesian two-step methods.
The paper tackled the problem of inefficient Bayesian two-step change point detection for Hawkes processes due to non-conjugacy, proposing a conjugate method via data augmentation that improves accuracy and efficiency, with experiments showing superior performance compared to baselines.
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.