We investigate the use of matrix completion methods for time-series imputation. Specifically we consider low-rank completion of the block-Hankel matrix representation of a time-series. Simulation experiments are used to compare the method with five recognised imputation techniques with varying levels of computational effort. The Hankel Imputation (HI) method is seen to perform competitively at interpolating missing time-series data, and shows particular potential for reproducing sharp peaks in the data.

翻译：本文研究了矩阵补全方法在时间序列插值中的应用。具体而言，我们探讨了时间序列的块汉克尔矩阵表示的低秩补全问题。通过仿真实验，将该方法与五种公认的插值技术在不同计算复杂度下进行比较。结果表明，汉克尔插补方法在填补缺失时间序列数据方面表现出竞争力，尤其在重现数据中的尖锐峰值方面展现出独特潜力。