A Modified Spatiotemporal Mixed-Effects Model for Interpolating Missing Values in Spatiotemporal Observation Data Series

Abstract

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.

A Weighted Surrogate Model for Spatio-Temporal Dynamics with Multiple Time Spans: Applications for the Pollutant Concentration of the Bai River

“…Ioannidis et al [3] proposed a graph-aware kernel kriged Kalman filtering (KKF) method accounting for the spatio-temporal variations. Nguyen et al [4] and Shi et al [5] modeled spatio-temporal dynamics using mixed-effects models. These methods only pay attention to spatio-temporal dynamics under fixed input parameters.…”

Spatio-Temporal Dynamic Fields Estimating and Modeling of Missing Points in Data Sets Using a Flexible State-Space Model

“…Process spatial-temporal dynamic data consists of two stages: A sensor data acquisition stage and a data processing stage, as demonstrated in Figure 1. In the data acquisition stage, due to the limitations of current technology and cost, missing data [4,5] may occur in each round of data collection, and its distribution may differ. As shown in the second subfigure, black indicates effective data collection and white indicates missing data.…”