高效长序列水位预测模型的研究与实现

Research and Implementation of Efficient Long Sequence Model for Water Level Forecasting

序列预测旨在利用历史序列模式信息预测未来长时间跨度的趋势,在工业领域具有众多的实际应用需求。针对工业数据序列预测问题中时序长度较长的特点,提出了一种高效的自注意力机制以适用于长序列数据建模与预测。该模型构建了新的嵌入表示,增加了池化操作,并且使用了生成式推断,实现长距离依赖建模和时序信号预测。相比之前的自注意力模型,该模型有效解决了现有方法在面对长序列预测时存在的预测精度不足、训练耗时过长等问题。在大规模水电站水轮机顶盖水位预测这一实际工业应用场景中,相比其他基准模型,该模型显著提高了长序列水位预测的精度和效率。

Long-Sequence forecasting aims to model and predict future long-term time series trends by leveraging historical knowledge and patterns and has many practical applications in various industries.To fully utilize long-time series industrial data characteristics,this paper presents an improved self-attention mechanism suitable for modeling and forecasting long sequence industrial data.Our model builds a new embedding representation learning module,combined with the pooling operations,and uses the generative inference for long-range dependency modeling and time-series signal prediction.Compared with the previous self-attention-based method,the proposed model effectively solves the problems of insufficient prediction accuracy and high training cost in long sequence prediction.Our model significantly improves long-sequence water level prediction accuracy and efficiency compared with other benchmark methods.Experiments conducted on the real-world water level data from a large-scale hydropower station proved the superior performance of the proposed model in terms of both effectiveness and efficiency over existing state-of-the-art models.