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:本条建议对删除- $d$ jacknife 进行概括化处理,以解决时间序列的超参数选择问题。 我称之为人工删除- $d$ jknife, 以强调这一方法取代经典删除步骤, 假冒删除, 将观察到的数据点替换为人为缺失值。 此程序保持数据顺序完整, 并允许与时间序列完全兼容。 此手稿无动于衷地使用这一方法, 并通过模拟研究展示其有限抽样优势。 此外, 本条通过规范高维预测汇率模型来描述其真实世界优势 。