TMF-GNN: Temporal matrix factorization-based graph neural network for multivariate time series forecasting with missing values

doi:10.1016/j.eswa.2025.127001

TMF-GNN: Temporal matrix factorization-based graph neural network for multivariate time series forecasting with missing values

2025-05-01

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DOI：10.1016/j.eswa.2025.127001

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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 anew 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.

Keywords:

Time series forecasting

Missing value

Graph neural network

TMF-GNN: Temporal matrix factorization-based graph neural network for multivariate time series forecasting with missing values

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