Study on Prediction of Zinc Grade by Transformer Model with De-Stationary Mechanism
Cheng Peng,
Liang Luo,
Hao Luo
et al.
Abstract:At present, in the mineral flotation process, flotation data are easily influenced by various factors, resulting in non-stationary time series data, which lead to overfitting of prediction models, ultimately severely affecting the accuracy of grade prediction. Thus, this study proposes a de-stationary attention mechanism based on the transformer model (DST) to learn non-stationary information in raw mineral data sequences. First, normalization processing is performed on matched flotation data and mineral grade… Show more
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