提出抗异常值的在线无限隐马尔可夫模型，提升数据漂移下的预测稳定性。
Doubly Outlier-Robust Online Infinite Hidden Markov Model
- 基于广义贝叶斯推断设计鲁棒更新规则，控制异常值影响
- 在真实交易、电力需求等数据上，预测误差降低最高达67%
- 平衡适应性与鲁棒性，适合对可靠性要求高的在线学习场景
针对流式数据含异常值且模型存在偏差的情况，本文推导了在线无限隐马尔可夫模型（iHMM）的鲁棒更新规则。通过近期广义贝叶斯推断中的后验影响函数（PIF）定义鲁棒性，并给出在线iHMM具有有界PIF的条件。引入鲁棒性会带来模式切换的适应滞后，为此提出批处理鲁棒iHMM（BR-iHMM），通过两个可调参数权衡适应性与鲁棒性。在订单簿数据、小时级电力需求及高维线性系统合成数据上，BR-iHMM相较现有在线贝叶斯方法，一步预测误差最高降低67%。结合有界PIF的理论保证，结果表明该方法在预测与可解释在线学习中具有实际价值。
原文摘要 · Abstract (English)
We derive a robust update rule for the online infinite hidden Markov model (iHMM) for when the streaming data contains outliers and the model is misspecified. Leveraging recent advances in generalised Bayesian inference, we define robustness via the posterior influence function (PIF), and provide conditions under which the online iHMM has bounded PIF. Imposing robustness inevitably induces an adaptation lag for regime switching. Our method, which is called Batched Robust iHMM (BR-iHMM), balances adaptivity and robustness with two additional tunable parameters. Across limit order book data, hourly electricity demand, and a synthetic high-dimensional linear system, BR-iHMM reduces one-step-ahead forecasting error by up to 67% relative to competing online Bayesian methods. Together with theoretical guarantees of bounded PIF, our results highlight the practicality of our approach for both forecasting and interpretable online learning.
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。