Research Article
Comparison of Machine Learning Algorithms for Sequential Dataset Prediction
@INPROCEEDINGS{10.1007/978-3-031-50580-5_33, author={Zhuang Ma and Tao Shen and Zhichao Sun and Kaining Xu and Xingsheng Guo}, title={Comparison of Machine Learning Algorithms for Sequential Dataset Prediction}, proceedings={Multimedia Technology and Enhanced Learning. 5th EAI International Conference, ICMTEL 2023, Leicester, UK, April 28-29, 2023, Proceedings, Part IV}, proceedings_a={ICMTEL PART 4}, year={2024}, month={2}, keywords={Deep learning Prediction Time series LSTM}, doi={10.1007/978-3-031-50580-5_33} }- Zhuang Ma
Tao Shen
Zhichao Sun
Kaining Xu
Xingsheng Guo
Year: 2024
Comparison of Machine Learning Algorithms for Sequential Dataset Prediction
ICMTEL PART 4
Springer
DOI: 10.1007/978-3-031-50580-5_33
Abstract
Accurate traffic flow prediction can provide basis for traffic control and travel planning. Accurate prediction is very important to the control and management of traffic flow in large cities. However, on the one hand, the traffic flow data information has the complicated interior space design relevance of discrete systems, that is, different kinds of connection relevance between different pavement nodes. On the other hand, it has dynamic duration correlation, that is, the spatial correlation of road nodes will change with time. At the same time, traffic flow data information generally shows a certain periodicity, but there are also some anomalies and specificity. According to XGBoost algorithm of artificial intelligence algorithm, LSTM optimization calculation method is created and compared. Based on the practical exploration of data information, it can be concluded that LSTM digital model can clearly predict traffic flow, and LSTM is better than traditional equipment learning model.
