Research on multivariate time series prediction method based on missing data imputation

Daozhou Wen; Xue Chen

doi:10.1117/12.3004892

9 October 2023 Research on multivariate time series prediction method based on missing data imputation

Daozhou Wen, Xue Chen

Proceedings Volume 12791, Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023); 1279127 (2023) https://doi.org/10.1117/12.3004892
Event: Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023), 2023, Qingdao, SD, China

Abstract

In multivariate time series (MTS), missing data will lead to inaccurate predictions. In this paper, we propose a multivariate time series forecasting model based on bidirectional gated recurrent neural networks (RNN) combining self-attention with residual connections, abbreviated as BGRRSA. At the same time, the model also uses univariate time series prediction to extract historical information from the target series. Finally, the outputs of the multi-level self-attention mechanism and univariate time series prediction are weighted and summed to obtain the model's final prediction. Experiments were conducted to compare and analyze different missing values imputation methods and different time series prediction methods on two datasets, verifying the effectiveness and accuracy of the model.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Daozhou Wen and Xue Chen "Research on multivariate time series prediction method based on missing data imputation", Proc. SPIE 12791, Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023), 1279127 (9 October 2023); https://doi.org/10.1117/12.3004892

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Data modeling

Windows

Matrices

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Machine learning

Autoregressive models

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