Long-term Sequence Analysis Of Climate Factors Based On Wavelet Analysis

Climate has a very big impact on human production and life.In today's highly developed science and technology,short-term weather forecasting is now very mature and accurate.However,forecasting monthly and inter-annual changes in a certain area cannot be predicted by predicting short-term weather.While analyzing and predicting long-term climatic factors in a certain area,it is possible to organize meteorological observation data for a period of time and arrange them according to time to form a time series.If the time series is within the predicted time range,there is no abrupt change and the randomly varying variance ?2 is small.And think that the evolutionary trend of the past and present will continue to be developed in the future.It can be predicted by time series correlation methods and model methods.Time series model analysis is widely used in the financial field.Whether it is a good description and prediction of the dynamic characteristics of interest rates,exchange rates,asset returns,etc.in the financial market,it can also be a good measure and assessment of market risks.The long-term sequence of climate elements is also time-varying,non-linear and a certain degree of randomness with the financial elements.Therefore,the time-series model analysis can be applied to analyze the climate elements.In addition,this paper combines the relevant methods of wavelet analysis to optimize the model and method of the time series model to further improve the prediction accuracy and reduce the prediction error.In this paper,we collected and averaged the average wind speed and average temperature in February of each year in a meteorological observatory in North China in 56 years.After introducing the relevant theories of time series analysis and wavelet analysis,this paper selected the time series model ARIMA model and GARCH model.Model identification and ordering,parameter estimation,and model suitability tests are used to model long-term climate factors.Then,on the basis of wavelet analysis,the time series was decomposed and reconstructed.The signals of each layer were modeled and predicted by ARIMA model.Finally,the time series was obtained based on the wavelet analysis by comparing the prediction errors of the three models.Forecasting has greatly improved prediction accuracy.