论文标题
具有投影预测推断的ARMA模型的贝叶斯秩序识别
Bayesian order identification of ARMA models with projection predictive inference
论文作者
论文摘要
自动回归运动平均（ARMA）型号是普遍存在的预测工具。这种模型中的简约因其可解释性和计算障碍性而受到高度重视，因此，模型订单的识别仍然是一项基本任务。我们通过投影预测性推断提出了一种新型的ARMA秩序识别方法，该推断通过使用参考模型从改善稳定性中受益。该过程由两个步骤组成：在第一个步骤中，从业人员将他们对基本数据生成过程的理解纳入参考模型，我们将其后来将其投影到可能是简约的子模型上。这些子模型是最佳推断的，以最好地复制参考模型的预测性能。我们进一步提出了对ARMA框架的搜索启发式。我们表明，通过我们的程序选择的子模型至少表现出与AIC所选择的模拟和真实数据实验所选择的标志，并且在某些情况下表现出色。最后，我们证明我们的过程对噪声是可靠的，并且可以很好地扩展到较大的数据。
Auto-regressive moving-average (ARMA) models are ubiquitous forecasting tools. Parsimony in such models is highly valued for their interpretability and computational tractability, and as such the identification of model orders remains a fundamental task. We propose a novel method of ARMA order identification through projection predictive inference, which benefits from improved stability through the use of a reference model. The procedure consists of two steps: in the first, the practitioner incorporates their understanding of underlying data-generating process into a reference model, which we latterly project onto possibly parsimonious submodels. These submodels are optimally inferred to best replicate the predictive performance of the reference model. We further propose a search heuristic amenable to the ARMA framework. We show that the submodels selected by our procedure exhibit predictive performance at least as good as those chosen by AIC over simulated and real-data experiments, and in some cases out-perform the latter. Finally we show that our procedure is robust to noise, and scales well to larger data.
