Refined Temperature Prediction Technology Based On Machine Learning And Data Mining

In this paper,a refined temperature forecasting method based on machine learning and data mining is developed based on a brief introduction of refined weather forecasting and the application status of data mining in meteorological field.The temperature historical observation data of three stations in northern China were collected and arranged.The time series data can be decomposed into the nature of the trend component and the fluctuation component.The trend of the temperature of the recursive neural network algorithm and the heuristic immune algorithm are respectively designed and implemented.The prediction models that were fitted after seasonal fluctuations were fitted and simulated on the MATLAB platform.The data of the previous 29 years of data set were trained on the network,and the final year's data was used for verification.By comparing with the historical facts,the accuracy and stability of the model forecasting are analyzed.It is proved that the refined temperature forecasting algorithm proposed in this paper is reasonable and feasible.It can improve the existing forecasting level and has important reference significance for the auxiliary decision-making of temperature forecasting.