Missing values in data series is a common problem in many research and applications. Most of existing interpolation methods are based on spatial or temporal interpolation, without considering the spatiotemporal correlation of observation data, resulting in poor interpolation effect. In this paper, a Modified Spatiotemporal Mixed-Effects (MSTME) model for interpolation of spatiotemporal data series is proposed. Experiments with simulated data and real SCIGN data are performed to assess the validity of the proposed model in comparison with Kriged Kalman Filter (KKF) model and Spatiotemporal Mixed-Effects (STME) model. The average improvements of simulated data and SCIGN data for observed stations are around 46% and 19% over the KKF model and 62% and 21% over the STME model, and those for unobserved stations are around 23% and 34% over the KKF model and 41% and 16% over the STME model, respectively, indicating that the proposed MSTME model can achieve better accuracy for interpolating missing values.

中文翻译：

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时空观测数据序列中缺失值的插值修正时空混合效应模型

在许多研究和应用中，数据序列中缺少值是一个普遍的问题。现有的大多数插值方法都是基于空间或时间插值的，没有考虑观测数据的时空相关性，导致插值效果差。本文提出了一种时空数据序列插值的修正时空混合效应模型（MSTME）。与Kriged Kalman滤波器（KKF）模型和时空混合效应（STME）模型相比，使用模拟数据和实际SCIGN数据进行了实验，以评估所提出模型的有效性。对于观测站，模拟数据和SCIGN数据的平均改进率比KKF模型高出约46％和19％，比STME模型高出62％和21％，