Research On Hybrid Forecasting Method And Application Of Time Series Under Decomposition-ensemble Framework

In recent years,with the rise of artificial intelligence and text mining technology,time series forecasting has become a hot topic in various fields.However,the unpredictable economic environment makes the characteristics and influencing factors of time series more complicated,which makes some achievements previously obtained may not meet the needs of people for time series forecasting in the new environment.Therefore,it is of great theoretical and application value to further explore and study time series forecasting methods on the basis of previous studies.Although many previous studies have shown that the decomposition and ensemble strategy is an effective forecasting framework for the forecasting of complex time series,most studies have ignored the research for decomposition methods,deep learning forecasting techniques,and multimodal predictors under the decomposition-ensemble framework.Therefore,to further improve the accuracy of time series forecasting,this paper carries out a series of research for decomposition method,deep mixed learning forecasting technology,multimodal predictor,and hybrid intelligent optimization algorithm.The specific research content and main conclusions include the following four aspects:(1)A series of statistical test methods are used to analyze the complex characteristics contained in the research data,so as to provide reference for the selection of methods in the following forecasting model.The experiment results show that the study data has the characteristics of nonlinear,chaotic,long memory,and recursion.Based on the test conclusions above,this paper adopts the decomposition-ensemble strategy to reduce data complexity in the subsequent forecasting model,and in terms of forecasting technology,this paper focuses on deep learning and hybrid forecasting technology that can capture complex sequence features.In addition,the multimodal predictors are selected from sources that lead to complex features.(2)Although previous many studies have used the idea of decomposition-ensemble to forecast time series,few studies have conducted systematic comparative studies for decomposition methods.In order to study the decomposition methods systematically,this paper constructs a mixed point forecasting model with chaos theory and different decomposition methods.The empirical study shows that the model with multivariate decomposition method has better forecasting performance than that of the model with univariate decomposition method.In univariate decomposition method,the prediction performance of the model with improved complete ensemble empirical mode decomposition with adaptive noise method is slightly better than that of the model with variational mode decomposition method,but is obviously better than that of the model with the other decomposition methods.At the same time,the forecasting performance of the model with multivariate variational mode decomposition is better than that of the model with multivariate empirical mode decomposition.(3)For univariate time series forecasting,and the problem that most researches on decomposition-ensemble forecasting focused on the single artificial intelligence forecasting technology,this paper chooses the optimal univariate decomposition method in the second part as the decomposition technology,and builds a mixed point forecasting model with deep hybrid forecasting technology and multi-objective hybrid intelligent optimization algorithm.The results show that the constructed model is optimal compared with the benchmark models.At the same time,among all the predictive technologies set up,the forecasting performance of the model with the deep mixed learning predictive technology constructed in this paper is the best,followed by the model with single deep learning,next the model with the machine learning,and finally the model with the econometric methods.In addition,to study the influence of different ensemble methods on the forecasting results,the single nonlinear integration and linear integration with multi-objective hybrid gray wolf optimization algorithm are also studied.The results show that the linear integration of multi-objective hybrid gray wolf optimization algorithm is slightly better than the nonlinear integration of single technique.(4)For the time series forecasting of multimodal influencing factors and the problem that most existing studies focus on value forecasting while ignoring interval forecasting,this paper chooses the optimal multivariable decomposition method in the second part as the decomposition technology,and a hybrid interval forecasting model with multimodal predictor and single objective hybrid intelligent optimization algorithm is constructed.The experimental results show that the constructed model is optimal compared with the benchmark models.Meantime,the performance of point forecasting and interval forecasting models with the “history + news+ finance” is better than that of the model with other predictors.The main contributions and innovations of this paper are as follows:As a whole,this paper combines the basic elements of forecasting models and the advantages of different methods to build a theoretical framework for time series forecasting,and from the perspective of methodology,each model can be independent of other models.From the perspective of specific constructed models,firstly,this paper constructs a forecasting model with chaos theory and different decomposition methods,and focuses on the decomposition effects of different decomposition methods.The research conclusions in this model are of great significance for the forecasting of non-stationary and highly fluctuating time series;in particular,it has important reference value for the choice of decomposition method in the research.This may be the first systematic study for univariate and multivariate decomposition methods.In addition,another innovation in the model is to construct a feature selection method with data chaos characteristics in the feature selection stage,and its advantage is that sequences with different chaos characteristics adopt different hysteresis order determination methods.Secondly,focusing on the research of forecasting technology,this paper constructs a forecasting model with deep hybrid forecasting technology and multi-objective hybrid intelligent optimization algorithm.The first innovation in the model is to build a deep hybrid forecasting technique with convolutional neural network and recurrent neural network of different feature.The second innovation is to optimize the performance of the original multi-objective grey wolf optimization algorithm by improving the linear decreasing convergence factor.Thirdly,focusing on the study of predictors,this paper constructs an interval forecasting model with different data modes and single objective hybrid intelligent optimization.The first innovation of the model is to study the interval forecasting performance of the model under the combination of multi-source predictors.The second innovation is to optimize the performance of the original single-objective gray wolf optimization algorithm by using the weight factor idea of dragonfly optimization algorithm and improving the linear decreasing convergence factor of original gray wolf optimization algorithm.