1. Cross-modal missing time-series imputation using dense spatio-temporal transformer nets
- Author
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Xusheng Qian, Teng Zhang, Meng Miao, Gaojun Xu, Xuancheng Zhang, Wenwu Yu, and Duxin Chen
- Subjects
time-series data missing ,complete data missing ,time-series data imputation ,cross-modal data fusion ,dense spatio-temporal transformer nets ,Biotechnology ,TP248.13-248.65 ,Mathematics ,QA1-939 - Abstract
Due to irregular sampling or device failure, the data collected from sensor network has missing value, that is, missing time-series data occurs. To address this issue, many methods have been proposed to impute random or non-random missing data. However, the imputation accuracy of these methods are not accurate enough to be applied, especially in the case of complete data missing (CDM). Thus, we propose a cross-modal method to impute time-series missing data by dense spatio-temporal transformer nets (DSTTN). This model embeds spatial modal data into time-series data by stacked spatio-temporal transformer blocks and deployment of dense connections. It adopts cross-modal constraints, a graph Laplacian regularization term, to optimize model parameters. When the model is trained, it recovers missing data finally by an end-to-end imputation pipeline. Various baseline models are compared by sufficient experiments. Based on the experimental results, it is verified that DSTTN achieves state-of-the-art imputation performance in the cases of random and non-random missing. Especially, the proposed method provides a new solution to the CDM problem.
- Published
- 2024
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