Abstract： As the ancient saying goes, history acts as a mirror. Drawing lessons from historical and empirical experience can make changes and prejudgment to the futuristic situation. A similar time sequence problem, which is related to trend prediction and trend analysis through historical data, can also happen in our modern life. This article is a research on time sequence related issues, a research on how to analyze and predict the futuristic change trend through historical statistical data. Based on neural network technology, by taking meteorological observation data as a research object, this article establishes an air temperature change time sequence prediction model. This mod-elling carries on characteristic processing on historical data through big data technology and learns complex nonlinear relationship between characteristic data and tag data through deep neural network automatically. Thus, an air temperature change trend prediction can be achieved. The experimental results show that the model mentioned above has more advantages than other model on time sequence prediction, which proves the feasibility of neural networks in meteorological prediction.
张恺 陈思. 基于神经网络的时序预测模型研究[J]. 电脑与电信, 2019, 1(1-2): 61-65.
ZHANG Kai CHEN Si. Research on Prediction Model for Time Series Based on Neural Networks. Computer & Telecommunication, 2019, 1(1-2): 61-65.
2019, Vol. 1 
