论文标题
RDI：对于不完整时间序列数据的自我训练的随机下降归因
RDIS: Random Drop Imputation with Self-Training for Incomplete Time Series Data
论文作者
论文摘要
在许多领域，例如医疗保健，气象和机器人技术，通常会遇到缺少值的时间序列数据。该插补旨在用有效的值填充缺失值。大多数插补方法都隐含地训练了模型，因为缺失值没有基础真相。在本文中，我们提出了自我训练（RDI）的随机下降插补，这是一种用于时间序列数据插图模型的新型培训方法。在RDI中，我们通过在不完整的数据中对观察到的值应用随机下降来生成额外的缺失值。我们可以通过填充随机删除的值来明确训练插补模型。此外，我们采用伪值的自我训练来利用原始的缺失值。为了提高伪值的质量，我们设置阈值并通过计算熵来过滤它们。为了验证RDI对时间序列归档的有效性，我们测试了RDI对各种插补模型进行测试，并在两个现实世界数据集上实现竞争成果。
Time-series data with missing values are commonly encountered in many fields, such as healthcare, meteorology, and robotics. The imputation aims to fill the missing values with valid values. Most imputation methods trained the models implicitly because missing values have no ground truth. In this paper, we propose Random Drop Imputation with Self-training (RDIS), a novel training method for time-series data imputation models. In RDIS, we generate extra missing values by applying a random drop on the observed values in incomplete data. We can explicitly train the imputation models by filling in the randomly dropped values. In addition, we adopt self-training with pseudo values to exploit the original missing values. To improve the quality of pseudo values, we set the threshold and filter them by calculating the entropy. To verify the effectiveness of RDIS on the time series imputation, we test RDIS to various imputation models and achieve competitive results on two real-world datasets.
