Prediction Algorithm for Heat Demand of Science and Technology Topics Based on Time Convolution Network
This addresses the problem of accurately forecasting topic popularity in science and technology demand data, which is incremental as it applies an existing method (TCN) to a new domain.
The paper tackled predicting heat demand for science and technology topics using a time convolution network (TCN) with self-attention, achieving better accuracy than other time series methods on real datasets.
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.