Research And Application Of Forecasting Method Based On "Decomposition-Reconstruction-Ensemble" Paradigm

The data in the ideal state generally has the characteristics of linearity,stability and low complexity.The accurate prediction of the data can not only provide decision support for investors,but also provide a reference basis for the government to formulate relevant policies.However,the real data show the characteristics of high complexity due to the influence of various external factors In addition,coupled with the impact of emergencies,the prediction of data becomes more and more difficult.Therefore,it is very important to find a reliable and effective method to predict data.At present,the methods of financial data prediction mainly include traditional econometric methods,artificial intelligence methods and decomposition integration methods Traditional econometric forecasting methods are powerless in the processing of nonlinearity,nonstationarity and some complex data Artificial intelligence methods solve some complex tasks by making machines simulate human intelligence,so as to obtain more accurate prediction.However,artificial intelligence methods are sensitive to parameters,easy to fall into local minimum,over fitting and so on Decomposition integration method is the mainstream method in current research.It decomposes complex data into simple components that are easy to describe,so as to reduce the difficulty of modeling,improve the prediction performance of the model,and achieve the purpose of "turning complexity into simplicity and breaking each one" However,in the existing decomposition integration methods,when there are too many components to predict each component separately,it will increase the calculation cost,and finally there may be the problem of error accumulation in the result integration.Aiming at the above problems,based on the idea of "decomposition first and then integration",this paper constructs a prediction method based on the "decompositionreconstruction-integration" paradigm from three aspects: data decomposition,component reconstruction and prediction method optimization.On this basis,relevant empirical research is carried out: Based on set empirical mode decomposition Reconstruction and particle swarm optimization of least squares support vector machine to predict the exchange rate of US dollar against RMB;Based on quadratic decomposition(CEEMDAN-CEEMDAN),reconstruction and chaotic sparrow search algorithm,kelm’s bitcoin price prediction is optimized;KELM’s crude oil futures price forecast is optimized based on ICEEMDAN-EMD,quadratic reconstruction and chaotic sparrow search algorithm.Using the error evaluation index and DM test,compared with the prediction results of other models,the empirical results show that the prediction results of the model constructed in this paper are better and more accurat.