Weather prediction method

Abstract

The invention discloses a weather prediction method, which includes the steps: providing temperature information values, normalizing the temperature information values, establishing an input / output matrix of a training sample, predicting weather through a neural network based on the output matrix, and the like. Original training data modes can be automatically distinguished by an improved algorithm, and are subjected to sample establishing and normalization. The weather prediction method is applicable to various complex circumstances, is high in flexibility, and needs no auxiliary data to complete prediction, and prediction results can be restored within a numeric range corresponding to original training data.

Description

technical field [0001] The invention mainly relates to technical fields such as weather forecasting and data mining. Background technique [0002] 1. Data mining technology: [0003] With the development of storage devices and database technology, data storage is no longer a problem. On the contrary, people have begun to feel overwhelmed by a large amount of data. Therefore, there is an urgent need for a scientific method to convert massive data into practically meaningful knowledge for people. And law, data mining is the technology produced under this background. [0004] In the late 1980s, data mining quietly emerged as a new research field. The purpose of data mining research is to discover those hidden information with specific rules that people are interested in in large data sets. With the development of data mining, this technology is applied in many fields such as business management, government office, scientific research and engineering development. [0005] In...

AI Technical Summary

Problems solved by technology

[0019] Looking back at the development of various data mining prediction algorithms, we can see that the bottleneck of their promotion is that different algorithms require different training data formats and specialized parameter settings, and the prediction process often requires human intervention and settings. A large amount of auxiliary data is provided, and the prediction results are not intuitive enough, which is very unfavorable for the promotion and use of the algorithm

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