This article proposes a generalisation of the delete-$d$ jackknife to solve hyperparameter selection problems for time series. I call it artificial delete-$d$ jackknife to stress that this approach substitutes the classic removal step with a fictitious deletion, wherein observed datapoints are replaced with artificial missing values. This procedure keeps the data order intact and allows plain compatibility with time series. This manuscript justifies the use of this approach asymptotically and shows its finite-sample advantages through simulation studies. Besides, this article describes its real-world advantages by regulating high-dimensional forecasting models for foreign exchange rates.

翻译：本文提出了一种删除-d刀切法的推广形式，用于解决时间序列的超参数选择问题。我将其称为人工删除-d刀切法，以强调该方法用虚构删除替代了经典的删除步骤——即用人工缺失值替换观测数据点。这种处理方式保持了数据顺序的完整性，并能直接兼容时间序列数据。本文从渐近角度论证了该方法的有效性，并通过模拟研究展示了其有限样本优势。此外，文章通过调整外汇汇率的高维预测模型，描述了该方法在实际应用中的优势。