When collecting the Internet of Things data using various sensors or other devices, it may be possible to miss several kinds of values of interest. In this paper, we focus on estimating the missing values in IoT time series data using three interpolation algorithms, including (1) Radial Basis Functions, (2) Moving Least Squares (MLS), and (3) Adaptive Inverse Distance Weighted. To evaluate the performance of estimating missing values, we estimate the missing values in eight selected sets of IoT time series data, and compare with those imputed by the standard k NN estimator. Our experiments indicate that in most experiments the estimation based on the Lancaster’s MLS is the best. It is also found that the number of nearest observed values for reference and the distribution of missing values could strongly affect the accuracy of imputation.

中文翻译：

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使用不同插值算法估计物联网时间序列数据中缺失值的比较

在使用各种传感器或其他设备收集物联网数据时，可能会遗漏几种感兴趣的值。在本文中，我们专注于使用三种插值算法估计 IoT 时间序列数据中的缺失值，包括 (1) 径向基函数、(2) 移动最小二乘法 (MLS) 和 (3) 自适应逆距离加权。为了评估估计缺失值的性能，我们估计了 8 组选定的 IoT 时间序列数据中的缺失值，并与标准 k NN 估计器估算的值进行比较。我们的实验表明，在大多数实验中，基于兰开斯特 MLS 的估计是最好的。还发现可供参考的最近观测值的数量和缺失值的分布会强烈影响插补的准确性。