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A literature review on the application of neural networks in the time series prediction
- 1 Nanjing Agricultural University
* Author to whom correspondence should be addressed.
Abstract
With the coming of the big data era, the demand for analysis and processing of mass data has become bigger and bigger. Prediction by using mass data has become an indispensable part of people’s daily lives and work, among which time series prediction has received widespread attention and been widely researched. Based on neural networks and recent years’ research on time series prediction, this article discusses the improvement and application of neural networks in time series prediction, including BP Neural Network, Long Short-term Memory Network (LSTM) and Neural Networks based on cluster analysis. By analyzing the purposes and consequences of these improved methods, this article clarifies the necessity and superiority of the (improved) neural networks in time series prediction. For example, through research and discussion, it finds that using improved neural networks for prediction can not only dramatically increase the accuracy of prediction, but also improve the anti-interference ability of the model. In other circumstances, improved neural networks can also use various methods to make the data match the model better in specific problems and so on. In the end, this article discusses the current problems and difficulties in neural networks in time series prediction, clarifying the problems that prediction needs a high match between model and data, the demand for the choice of model is high and so on. Then it makes prospects for future research, expecting the appearance of more models and improved methods.
Keywords
Neural Networks, time series prediction, BP Neural Network, LSTM, cluster analysis
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Cite this article
Pan,J. (2024). A literature review on the application of neural networks in the time series prediction. Applied and Computational Engineering,64,198-202.
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Volume title: Proceedings of the 6th International Conference on Computing and Data Science
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