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Study of combination forecasting in Airline Traffic Turnover

Journal of Systems Science and Systems Engineering 2001年第2期13卷 233-239页

作者： LIU Jun, QIU Wan-hua, WEI Cun-pingSchool of Management, Beijing University of Aeronautics and Astronautics, Beijing 100083, China School of Management Beijing University of Aeronautics and Astronautics Beijing 100083 China

The basic theory and method of the combination forecasting are introduced. Based on the actual data in an airline, the case study was presented. In the case study, two basic forecasting models are set up, which are the time-regression plus seasonal factor model and the logarithm additive Winters model. And two combination models are established with the basic models, which are the optimal combination model and the regressive combination model. The results of the study are guidable to the practice.

关键词： forecasting combination forecasting air transportation forecasting

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Nonlinear combination forecasting of measles incidence in Shenyang based on General Regression Neural Network

Nonlinear combination forecasting of measles incidence in Sh...

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第32届中国控制与决策会议

作者： Enbin Yang Dongyu Yan Qicheng Xu Zhenhao Wang Shitong Liu School of science Shenyang Jianzhu University School of Municipal and Environmental Engineering Shenyang Jianzhu University

Accurate prediction of the incidence of measles is an important prerequisite for the prevention and treatment of infectious *** to use a combined prediction ***,analyze the seasonal characteristics of measles to establish the seasonal autoregressive integral sliding average ***,use the residual correction method to optimize the gray model to obtain better fitting ***,input the predicted values of the optimized single-item model to the General Regression Neural Network to output the final measles incidence predictive *** results show that the combined forecasting model is accurate for trend prediction,the surplus sum of squares,rms error,mean absolute error and mean absolute percent error are lower than the single prediction model,so the combined forecasting model’s prediction effect is good.

关键词： Incidence Autoregressive Integrated Moving Average Model Residual error correction combination forecasting General Regression Neural Network

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Online Clustering for Wind Speed forecasting Based on combination of RBF Neural Network and Persistence Method

Online Clustering for Wind Speed Forecasting Based on Combin...

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2011 Chinese Control and Decision Conference（CCDC）

作者： Xiao Qin is with Department of Automation,Tsinghua University,Beijing,China.Cong Jiang is with Department of Systems Engineering and Engineering Management,The Chinese University of Hong Kong,Shatin,Hong Kong SAR,China.Jun Wang is with Department of Control Science and Engineering,Tongji University,Shanghai,China.

This paper proposes an online clustering algorithm for wind speed *** algorithm combines the persistence method and the RBF neural network,and chooses an appropriate method according to different wind *** simulations demonstrate that this algorithm can more accurately predict wind speed than either of the single methods and therefore is more effective for wind speed forecasting.

关键词： Wind speed prediction combination forecasting On-line prediction Clustering algorithm

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A combined forecasting method for intermittent demand using the automotive aftermarket data

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Data Science and Management 2022年第2期5卷 43-56页

作者： Xiaotian Zhuang Ying Yu Aihui Chen Department of Intelligent Supply Chain Jingdong Zhenshi Information Technology Co.Ltd.Beijing100176China College of Management and Economics Tianjin UniversityTianjin300072China Qingdao Institute for Ocean Engineering of Tianjin University Qingdao266207China

Intermittent demand forecasting is an important challenge in the process of smart supply chain transformation,and accurate demand forecasting can reduce costs and increase efficiency for *** study proposes an intermittent demand combination forecasting method based on internal and external data,builds intermittent demand feature engineering from the perspective of machine learning,predicts the occurrence of demand by classification model,and predicts non-zero demand quantity by regression *** on the strategy selection on the inventory side and the stocking needs on the replenishment side,this study focuses on the optimization of the classification problem,incorporates the internal and external data of the enterprise,and proposes two combination forecasting optimization methods on the basis of the best classification threshold searching and transfer learning,*** on the real data of auto after-sales business,these methods are evaluated and validated in multiple *** with other intermittent forecasting methods,the models proposed in this study have been improved significantly in terms of classification accuracy and forecasting precision,which validates the potential of combined forecasting framework for intermittent demand and provides an empirical study of the framework in industry *** results show that this research can further provide accurate upstream inputs for smart inventory and guarantee intelligent supply chain decision-making in terms of accuracy and efficiency.

关键词： Intelligent supply chain management Intermittent demand combination forecasting Machine learning Transfer learning

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Research on the Method of Nonlinear Combining Forecasts based on Fuzzy System

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Systems Science and Systems Engineering 2000年第3期10卷 301-307页

作者： DONG Jing\|rong College of Management, Chongqing University, Chongqing 400044, China

In this paper, a new nonlinear combination forecasting method based on fuzzy system is presented to overcome some limitation in linear combination forecasting. Furthermore, the corresponding genetic learning algorithm is employed to identify the parameter of the fuzzy system model and partition of fuzzy subsets. Theoretical analysis and forecasting examples all show that the new technique has reinforcement learning properties and universalized capabilities. With respect to combined modeling and forecasting of non stationary time series in nonlinear systems, which has some uncertainties, the method is more accurate and reasonable than other existing combining methods which are based on linear combination of forecasts.

关键词： combination forecasting fuzzy system genetic algorithm

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A combination Method for Wind Power Prediction Based on Cooperative Game Theory

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Electricity 2014年第2期25卷 36-40页

作者： Li Kangping Gao Yajing North China Electric Power University

Accurate prediction of wind power is significant for power system dispatching as well as safe and stable operation. By means of BP neural network, radial basis function neural network and support vector machine, a new combined method of wind power prediction based on cooperative game theory is proposed. In the method, every single forecasting model is regarded as a member of the cooperative games, and the sum of square error of combination forecasting is taken as the result of cooperation. The result is divided among the members according to Shapley values, and then weights of combination forecasting can be obtained. Application results in an actual wind farm show that the proposed method can effectively improve prediction precision.

关键词： wind power combination forecasting cooperative game theory

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基于经济增长的新疆能源消费趋势及影响因素研究

基于经济增长的新疆能源消费趋势及影响因素研究

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作者：郑学进新疆财经大学

学位级别：硕士

Since the reform and open policy, Xinjiang's economic development speed, energy source as the driving force of economic development,the demand is also increased significantly, which led to the total energy consumption of Xinjiang increasing. For a long time xinjiang's economic growth is extensive, high input and low output growth pattern,which not only resulted in the waste of energy and resources, and result in the pollution of the environment, brought huge environmental problems. The sustained and stable development of energy, is of great significance to economic growth. To ensure the sustained and stable development of energy, rational energy development strategy, there must be reasonable expectation of Xinjiang energy consumption trend, grasp the trend of energy consumption of Xinjiang, find the influence factors of energy consumption in *** paper is based on the qualitative analysis and the quantitative analysis,by analysis the energy consumption and economic growth of Xinjiang, the forecast of energy consumption trend in Xinjiang by using the method of combination, and the influence factors of energy consumption in Xinjiang by multiple regression analysis, finally, according to the status of Xinjiang energy and economic development, put forward the policy recommendations of Xinjiang's energy *** is a close relationship between energy consumption and economic growth,through the research on the relationship between energy consumption and economic growth in Xinjiang, a one-way causal relationship was found between energy consumption and economic growth, the economic growth of Xinjiang is the causes of energy consumption increase. Under the background of economic growth, the prediction of the energy consumption trends in Xinjiang by combination,found that Xinjiang future energy consumption will be about 11% of the annual growth rate of development. Xinjiang energy consumption increase, bring great challenges to Xinjiang's energy supply, in

关键词： Energy consumption combination forecasting Economic growth Influence factors Xinjiang

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