A Research On Regression Analysis Model And Dynamic Optimization Mechanism For Data Prediction

Data prediction provides the decision-making systems with the necessary future information for making decisions,and the data analysis and processing occupy an extremely important position in the development of prediction theory.By grasping the law of development in advance,we can provide the reliable support for decision-making through the trend of data changes.Although there are a large number of data prediction problems in real life,it is often unable to make scientific and accurate predictions of data due to the lack of forecasting technology.In recent years,although the research on traditional prediction theories and methods have made great progress,due to the complexity and uncertainty of the environment in which the problem is predicted,and the limitations of traditional data analysis models,the results of the theoretical research and the actual forecast will be affected to the varying degrees.Therefore,how to make all data meaningful and make better predictions based on these data has higher research value.In addition,the regression analysis model is limited by its adaptability and processing capabilities.As the demand for problems and complexity increase,it becomes more difficult to optimize the regression model using traditional methods.Therefore,the article compares and analyzes the practical effects of the existing optimization algorithms,tries to improve them,and forms a targeted model optimization mechanism.This dissertation starts with the concrete problems that the practical problem solutions may encounter while people use the existing optimization mechanism to solve the practical models,and makes further extensive study on the regression analysis models,static and dynamic environment optimization mechanisms and practical applications of data prediction from different aspects.The details are given as follows.Firstly,we explored and established an effective nonlinear regression analysis model from various perspectives.By comparing and analyzing various model,a relatively reliable analysis model and regression equation are produced.And then we will use different evaluation indicators to evaluate the model.Secondly,by establishing the corresponding relationship between individual selection methods and different search strategies in a multi-group mechanism,we propose a multi-group hybrid optimization mechanism for the static environments.And on this basis,we also have designed another optimization mechanism of variable multi-neighborhood structure for the dynamic environments.Finally,in order to be able to solve the problem of data prediction in the life.This dissertation uses the forecasting application of household power data as an example to establish a variety of forecasting models.Then we use two optimization mechanisms to estimate the model parameters.The experiment results show that both mechanisms exhibit a good performance.The extensive research results presented above not only enrich the theoretical research of data analysis model but also widen the applied areas of the practical application of prediction model.In particular,for the two model optimization mechanisms are designed in this paper,it not only puts forward new thinking for the study on the prediction method of data driven,but also solves the practical problems of household power forecasting in this production and life effectively.