论文标题
基于时间卷积网络的科学和技术主题的热量需求的预测算法
Prediction Algorithm for Heat Demand of Science and Technology Topics Based on Time Convolution Network
论文作者
论文摘要
由于深度学习的快速发展，大数据分析技术不仅在自然语言处理领域被广泛使用，而且在数值预测领域也更加成熟。这对于科学和技术需求数据的主题预测和分析是重要的意义。如何应用主题功能准确预测科学和技术需求的主题热量是解决此问题的核心。在本文中，提出了一种基于时间卷积网络（TCN）的科学和技术需求热量的预测方法，以获得科学和技术需求的主题特征代表。时间序列预测是基于TCN网络和自我注意力机制进行的，这提高了科学和技术需求数据需求数据实验的准确性表明，该算法的预测准确性比真实科学和技术需求数据集的其他时间序列预测方法更好。
Thanks to the rapid development of deep learning, big data analysis technology is not only widely used in the field of natural language processing, but also more mature in the field of numerical prediction. It is of great significance for the subject heat prediction and analysis of science and technology demand data. How to apply theme features to accurately predict the theme heat of science and technology demand is the core to solve this problem. In this paper, a prediction method of subject heat of science and technology demand based on time convolution network (TCN) is proposed to obtain the subject feature representation of science and technology demand. Time series prediction is carried out based on TCN network and self attention mechanism, which increases the accuracy of subject heat prediction of science and technology demand data Experiments show that the prediction accuracy of this algorithm is better than other time series prediction methods on the real science and technology demand datasets.
