Missing value interpolation algorithm for long-term temperature observation data based on data augmentation multiple interpolation method - Xiaolin Liu; Bo Wang; Shuanglong Jin; Zongpeng Song (Results in Engineering, 2025)
Temperature data exhibit obvious heterogeneity both in time and space. Long-term missing data can disrupt the continuous spatiotemporal heterogeneity and periodic characteristics of the original data, making it difficult to calculate the initial estimated value of the data. This leads to the randomness of the initial value during the interpolation process and reduces the coefficient of determination of the data after interpolation. For this purpose, an algorithm for interpolating missing values in long-term temperature observation data was designed. The noise reduction method based on Graph Attention Network (GAT) is adopted to handle the noise in the data. The graph structure is utilized to represent the time dependence and correlation of temperature data, and the information propagation weight between nodes is dynamically adjusted through the attention mechanism to improve the data quality. The Expectation Maximization (EM) algorithm is used to initially interpolate the missing data and obtain the initial estimated value. Based on the Data Augmentation (DA) multiple interpolation method, the Bayesian model is used to repair the missing data of long-term temperature observation data. Fully considering the characteristics of time series, the temperature data status is continuously updated through the recursive correction process. Taking a certain forest ecological station as the research area, the missing value interpolation performance of the temperature observation dataset of a certain forest ecological station with a data time span from May 1st to May 10th, 2023 was verified. The experimental results show that the mean square error of the interpolated data is below 0.2, the coefficient of determination is between 0.91 and 0.95, which is closer to 1, and the interpolation time is <5 s. This method can provide a more accurate basis for subsequent data analysis and application.