Prediction Algorithm for Heat Demand of Science and Technology Topics Based on Time Convolution Network

TL;DR

This paper proposes a novel prediction method using Time Convolution Network and self-attention to accurately forecast the heat demand of science and technology topics, demonstrating superior performance over existing methods.

Contribution

Introduces a TCN-based approach combined with self-attention for science and technology demand heat prediction, improving accuracy in time series forecasting.

Findings

Outperforms other time series prediction methods on real datasets

Achieves higher prediction accuracy for science and technology demand heat

Effectively captures features of demand data using TCN and self-attention

Abstract

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.