Abstract
The production data in the industrial field have the characteristics of multimodality, high dimensionality and large correlation differences between attributes. Existing data prediction methods cannot effectively capture time series and modal features, which leads to prediction hysteresis and poor prediction stability. Aiming at the above problems, this paper proposes a time-series and modal feature enhancement method based on a dual-stage self-attention mechanism (DATT), and a time series prediction method based on a gated feedforward recurrent unit (GFRU). On this basis, the DATT-GFRU neural network with a gated feedforward recurrent neural network and dual-stage self-attention mechanism is designed and implemented. Experiments show that the prediction effect of the neural network prediction model based on DATT is significantly improved. Compared with the traditional prediction model, the DATT-GFRU neural network has a smaller average error of model prediction results, stable prediction performance, and strong generalization ability on the three datasets with different numbers of attributes and different training sample sizes.
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Acknowledgements
This work is financially supported by: The National Key R&D Program of China (No. 2020YFB1712600); The Fundamental Research Funds for Central University (No. 3072022QBZ0601); The National Natural Science Foundation of China (No. 62272126); and The National Natural Science Foundation of China (No. 61872104).
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Liu, X. et al. (2023). Research on Multi-Modal Time Series Data Prediction Method Based on Dual-Stage Attention Mechanism. In: Yu, Z., et al. Data Science. ICPCSEE 2023. Communications in Computer and Information Science, vol 1879. Springer, Singapore. https://doi.org/10.1007/978-981-99-5968-6_10
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DOI: https://doi.org/10.1007/978-981-99-5968-6_10
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