To solve the medium and long term power load forecasting problem,the combination forecasting method is further expanded and a weighted combination forecasting model for power load is put forward.This model is divided ...To solve the medium and long term power load forecasting problem,the combination forecasting method is further expanded and a weighted combination forecasting model for power load is put forward.This model is divided into two stages which are forecasting model selection and weighted combination forecasting.Based on Markov chain conversion and cloud model,the forecasting model selection is implanted and several outstanding models are selected for the combination forecasting.For the weighted combination forecasting,a fuzzy scale joint evaluation method is proposed to determine the weight of selected forecasting model.The percentage error and mean absolute percentage error of weighted combination forecasting result of the power consumption in a certain area of China are 0.7439%and 0.3198%,respectively,while the maximum values of these two indexes of single forecasting models are 5.2278%and 1.9497%.It shows that the forecasting indexes of proposed model are improved significantly compared with the single forecasting models.展开更多
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 th...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.展开更多
A new kind of combining forecasting model based on the generalized weighted functional proportional mean is proposed and the parameter estimation method of its weighting coefficients by means of the algorithm of quadr...A new kind of combining forecasting model based on the generalized weighted functional proportional mean is proposed and the parameter estimation method of its weighting coefficients by means of the algorithm of quadratic programming is given. This model has extensive representation. It is a new kind of aggregative method of group forecasting. By taking the suitable combining form of the forecasting models and seeking the optimal parameter, the optimal combining form can be obtained and the forecasting accuracy can be improved. The effectiveness of this model is demonstrated by an example.展开更多
Two kinds of parameter estimation methods (I) and (II) of combining forecasting based on harmontic mean are proposed and compared through a lot of simulation forecasting examples. A very helpful conclusion is obtained...Two kinds of parameter estimation methods (I) and (II) of combining forecasting based on harmontic mean are proposed and compared through a lot of simulation forecasting examples. A very helpful conclusion is obtained, which can lay solid foundations for correct application of the above methods.展开更多
This paper proposes artificial neural networks (ANN) as a tool for nonlinear combination of forecasts. In this study, three forecasting models are used for individual forecasts, and then two linear combining methods a...This paper proposes artificial neural networks (ANN) as a tool for nonlinear combination of forecasts. In this study, three forecasting models are used for individual forecasts, and then two linear combining methods are used to compare with the ANN combining method. The comparative experiment using real--world data shows that the prediction by the ANN method outperforms those by linear combining methods. The paper suggests that the ANN combining method can be used as- an alternative to conventional linear combining methods to achieve greater forecasting accuracy.展开更多
This paper mainly discusses the nonlinear combination forecasting model and states that the nonlinear combination forecasting model is better than linear combination forecasting model in many aspect.
Hierarchical time series arise in manufacturing and service industries when the products or services have the hierarchical structure,and top-down and bottomup methods are commonly used to forecast the hierarchical tim...Hierarchical time series arise in manufacturing and service industries when the products or services have the hierarchical structure,and top-down and bottomup methods are commonly used to forecast the hierarchical time series.One of the critical factors that affect the performance of the two methods is the correlation between the data series.This study attempts to resolve the problem and shows that the top-down method performs better when data have high positive correlation compared to high negative correlation and combination of forecasting methods may be the best solution when there is no evidence of the correlationship.We conduct the computational experiments using 240 monthly data series from the‘Industrial’category of the M3-Competition and test twelve combination methods for the hierarchical data series.The results show that the regression-based,VAR-COV and the Rank-based methods perform better compared to the other methods.展开更多
As the participants of Earth Orientation Parameters Combination of Prediction Pilot Project(EOPC PPP),Sternberg Astronomical Institute of Moscow State University(SAI) and Shanghai Astronomical Observatory(SHAO) have a...As the participants of Earth Orientation Parameters Combination of Prediction Pilot Project(EOPC PPP),Sternberg Astronomical Institute of Moscow State University(SAI) and Shanghai Astronomical Observatory(SHAO) have accumulated ~1800 days of Earth Orientation Parameters(EOP) predictions since2012 till 2017, which were up to 90 days into the future, and made by four techniques: auto-regression(AR), least squares collocation(LSC), and neural network(NNET) forecasts from SAI, and least-squares plus auto-regression(LS+AR) forecast from SHAO. The predictions were finally combined into SAISHAO COMB EOP prediction. In this work we present five-year real-time statistics of the combined prediction and compare it with the uncertainties of IERS bulletin A predictions made by USNO.展开更多
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...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.展开更多
This paper presents the parameter estimation methods of weighting coefficients in generalized weighted mean combining forecasting, and uses this forecasting model to forecast air materials consumption. Finially, the e...This paper presents the parameter estimation methods of weighting coefficients in generalized weighted mean combining forecasting, and uses this forecasting model to forecast air materials consumption. Finially, the efficiency of generalized weighted mean combining forecasting has been demonstrated by an example.展开更多
This paper investigates the private motor vehicle market in China, which has been developed since 1984. Combined forecasting for the number of motor vechicles owned by individuals is made from several least squares re...This paper investigates the private motor vehicle market in China, which has been developed since 1984. Combined forecasting for the number of motor vechicles owned by individuals is made from several least squares regression equations and a Logistic model. Regional analysis is made on the data of the thirty areas by hierarchical cluster, revealing various types of the development of the regional markets.展开更多
文摘To solve the medium and long term power load forecasting problem,the combination forecasting method is further expanded and a weighted combination forecasting model for power load is put forward.This model is divided into two stages which are forecasting model selection and weighted combination forecasting.Based on Markov chain conversion and cloud model,the forecasting model selection is implanted and several outstanding models are selected for the combination forecasting.For the weighted combination forecasting,a fuzzy scale joint evaluation method is proposed to determine the weight of selected forecasting model.The percentage error and mean absolute percentage error of weighted combination forecasting result of the power consumption in a certain area of China are 0.7439%and 0.3198%,respectively,while the maximum values of these two indexes of single forecasting models are 5.2278%and 1.9497%.It shows that the forecasting indexes of proposed model are improved significantly compared with the single forecasting models.
文摘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.
文摘A new kind of combining forecasting model based on the generalized weighted functional proportional mean is proposed and the parameter estimation method of its weighting coefficients by means of the algorithm of quadratic programming is given. This model has extensive representation. It is a new kind of aggregative method of group forecasting. By taking the suitable combining form of the forecasting models and seeking the optimal parameter, the optimal combining form can be obtained and the forecasting accuracy can be improved. The effectiveness of this model is demonstrated by an example.
文摘Two kinds of parameter estimation methods (I) and (II) of combining forecasting based on harmontic mean are proposed and compared through a lot of simulation forecasting examples. A very helpful conclusion is obtained, which can lay solid foundations for correct application of the above methods.
文摘This paper proposes artificial neural networks (ANN) as a tool for nonlinear combination of forecasts. In this study, three forecasting models are used for individual forecasts, and then two linear combining methods are used to compare with the ANN combining method. The comparative experiment using real--world data shows that the prediction by the ANN method outperforms those by linear combining methods. The paper suggests that the ANN combining method can be used as- an alternative to conventional linear combining methods to achieve greater forecasting accuracy.
文摘This paper mainly discusses the nonlinear combination forecasting model and states that the nonlinear combination forecasting model is better than linear combination forecasting model in many aspect.
基金This work was supported by Program of Shanghai Subject Chief Scientist[16XD1401700]National Natural Science Foundation of China[71421002].
文摘Hierarchical time series arise in manufacturing and service industries when the products or services have the hierarchical structure,and top-down and bottomup methods are commonly used to forecast the hierarchical time series.One of the critical factors that affect the performance of the two methods is the correlation between the data series.This study attempts to resolve the problem and shows that the top-down method performs better when data have high positive correlation compared to high negative correlation and combination of forecasting methods may be the best solution when there is no evidence of the correlationship.We conduct the computational experiments using 240 monthly data series from the‘Industrial’category of the M3-Competition and test twelve combination methods for the hierarchical data series.The results show that the regression-based,VAR-COV and the Rank-based methods perform better compared to the other methods.
基金supported by Discipline Innovative Engineering Plan of Modern Geodesy and Geodynamics(grant No.B17033)NSFC grants(11673049,11773057)RFBR grant(N16-05-00753)
文摘As the participants of Earth Orientation Parameters Combination of Prediction Pilot Project(EOPC PPP),Sternberg Astronomical Institute of Moscow State University(SAI) and Shanghai Astronomical Observatory(SHAO) have accumulated ~1800 days of Earth Orientation Parameters(EOP) predictions since2012 till 2017, which were up to 90 days into the future, and made by four techniques: auto-regression(AR), least squares collocation(LSC), and neural network(NNET) forecasts from SAI, and least-squares plus auto-regression(LS+AR) forecast from SHAO. The predictions were finally combined into SAISHAO COMB EOP prediction. In this work we present five-year real-time statistics of the combined prediction and compare it with the uncertainties of IERS bulletin A predictions made by USNO.
基金SupportedbyNationalNaturalscienceFoundationofChina (No .79770 10 5 )
文摘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.
文摘This paper presents the parameter estimation methods of weighting coefficients in generalized weighted mean combining forecasting, and uses this forecasting model to forecast air materials consumption. Finially, the efficiency of generalized weighted mean combining forecasting has been demonstrated by an example.
文摘This paper investigates the private motor vehicle market in China, which has been developed since 1984. Combined forecasting for the number of motor vechicles owned by individuals is made from several least squares regression equations and a Logistic model. Regional analysis is made on the data of the thirty areas by hierarchical cluster, revealing various types of the development of the regional markets.