TMF-GNN: Temporal matrix factorization-based graph neural network for multivariate time series forecasting with missing values
Suhyeon Kim, Taek-Ho Lee, Junghye Lee · Expert Systems with Applications · 2025
Missing data in multivariate time series (MTS) is a very common issue, often caused by unreliable sensors and data storage or transmission problems. Particularly, such missing data can cause some errors and biases in the MTS forecasting tasks of real-world applications, implying that proper handling of the missing data is essential. Therefore, in this study, we propose a new method for MTS forecasting with missing values, called a temporal matrix factorization-based graph neural network (TMF-GNN), to improve predictive performance outcomes. TMF-GNN basically uses the concept of TMF, which reconstructs partially observed MTS data. We newly present a data-adaptive regularization method for TMF based on graph-based and sequential deep learning algorithms to capture both the variable-wise and time-wise information of MTS data affected by missingness. We demonstrate the feasibility of the proposed method by conducting various experiments on three MTS datasets and show how it outperforms baseline methods. We believe that our study will have an impact on several MTS-related tasks and that it can be a useful alternative for handling missing values in MTS data. • Missing data is a pervasive problem in multivariate time series forecasting. • We proposed a method to optimize time series forecasting with missing data. • We proposed an approach using graph-based and temporal deep learning models. • The proposed method outperformed existing methods in forecasting accuracy. • Our approach can mine temporal patterns and inter-correlations in time series data.