A nonlinear combination forecasting method based on the fuzzy inference system

It has been shown in recent economic and statistical studies that combining forecasts may produce more accurate forecasts than individual ones. However, the literature on combining forecasts has almost exclusively focused on linear combining forecasts. In this paper, a new nonlinear combination forecasting method based on fuzzy inference system is present to overcome the difficulties and drawbacks in linear combination modeling of non-stationary time series. Furthermore, the optimization algorithm based on a hierarchical structure of learning automata is used to identify the parameters of the fuzzy system. Experiment results related to numerical examples demonstrate that the new technique has excellent identification performances and forecasting accuracy superior to other existing linear combining forecasts.

A Weighted Combination Forecasting Model for Power Load Based on Forecasting Model Selection and Fuzzy Scale Joint Evaluation

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.

An Investigation of Coal Demand in China Based on the Variable Weight Combination Forecasting Model

Variable weight combination forecasting combines individual forecasting models after giving them proper weights at each time point. Weight is the type of function that changes with forecast time. A relatively rational description of the system can be proposed with the forecasting method, which is of higher precision and better stability. Two individual forecasting models, grey system forecasting and multiple regression forecasting, were generated based on the historical data and influencing factors of coal demand in China from 1981 to 2008. According to the theory of combination forecasting, the variable weight combination forecasting model was formulated to forecast coal demand in China for the next 12 years.

Study of Combination Forecasting in Airline Traffic Turnover

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.

The Optimal Weighted Combinational Forecasting with Constant Terms

We propose a model based on the optimal weighted combinational forecasting with constant terms, give formulae of the weights and the average errors as well as a relation of the model and the corresponding model without constant terms, and compare these models. Finally an example was given, which showed that the fitting precision has been enhanced.

Nonlinear combined forecasting model based on fuzzy adaptive variable weight and its application

In order to enhance forecasting precision of problems about nonlinear time series in a complex industry system,a new nonlinear fuzzy adaptive variable weight combined forecasting model was established by using conceptions of the relative error,the change tendency of the forecasted object,gray basic weight and adaptive control coefficient on the basis of the method of fuzzy variable weight.Based on Visual Basic 6.0 platform,a fuzzy adaptive variable weight combined forecasting and management system was developed.The application results reveal that the forecasting precisions from the new nonlinear combined forecasting model are higher than those of other single combined forecasting models and the combined forecasting and management system is very powerful tool for the required decision in complex industry system.

Generalized weighted functional proportional mean combining forecasting model and its method of parameter estimation

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.

Research on Methods of Parameter Estimation in Combining Forecasting Based on Harmonic Mean

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.

Artificial Neural Network for Combining Forecasts

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.

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

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 enterprises.This 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 model.Based 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,respectively.Based on the real data of auto after-sales business,these methods are evaluated and validated in multiple dimensions.Compared 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 practice.The 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.

Nonlinear Combination Forecasting Model and Its Application

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.

Individual and combination approaches to forecasting hierarchical time series with correlated data:an empirical study

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.

Quadratic Radical Function Better Than Fisher z Transformation

A new explicit quadratic radical function is found by numerical experiments,which is simpler and has only 70.778%of the maximal distance error compared with the Fisher z transformation.Furthermore,a piecewise function is constructed for the standard normal distribution:if the independent variable falls in the interval(-1.519,1.519),the proposed function is employed;otherwise,the Fisher z transformation is used.Compared with the Fisher z transformation,this piecewise function has only 38.206%of the total error.The new function is more exact to estimate the confidence intervals of Pearson product moment correlation coefficient and Dickinson best weights for the linear combination of forecasts.

Combined SAI-SHAO prediction of Earth Orientation Parameters since 2012 till 2017

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.

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

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.

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

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.

Dynamic-statistics combined forecast scheme based on the abrupt decadal change component of summer precipitation in East Asia

Based on the 1983~2011 CMAP data,the precipitation anomaly in East Asia and its nearby sea regions（hereafter called East Asia for short） demonstrates the ＂＋-＋＂ pattern before 1999 and the ＂-＋-＂ pattern afterwards; this decadal change is contained principally in the corresponding EOF3 component.However,the NCC_CGCM forecast results are quite different,which reveal the ＂＋-＋-＂ pattern before 1999 and the ＂-＋-＋＂ pattern afterwards.Meanwhile,the probability of improving NCC_CGCM＇s forecast accuracy based on these key SST areas is discussed,and the dynamic-statistics combined forecast scheme is constructed for increasing the information of decadal change contained in the summer precipitation in East Asia.The independent sample forecast results indicate that this forecasting scheme can effectively modify the NCC_CGCM＇s decadal change information contained in the summer precipitation in East Asia（especially in the area of 30°N–55°N）.The ACC is 0.25 and ACR is 61% for the forecasting result based on the V SST area,and the mean ACC is 0.03 and ACR is 51% for the seven key areas,which are better than NCC_CGCM＇s system error correction results（ACC is -0.01 and ACR is 49%）.Besides,the modified forecast results also provide the information that the precipitation anomaly in East Asia mainly shows the ＂＋-＋＂ pattern before 1999 and the ＂-＋-＂ pattern afterwards.

Forecasting Gas Consumption Based on a Residual Auto-Regression Model and Kalman Filtering Algorithm

Consumption of clean energy has been increasing in China.Forecasting gas consumption is important to adjusting the energy consumption structure in the future.Based on historical data of gas consumption from 1980 to 2017,this paper presents a weight method of the inverse deviation of fitted value,and a combined forecast based on a residual auto-regression model and Kalman filtering algorithm is used to forecast gas consumption.Our results show that:(1)The combination forecast is of higher precision:the relative errors of the residual auto-regressive model,the Kalman filtering algorithm and the combination model are within the range(–0.08,0.09),(–0.09,0.32)and(–0.03,0.11),respectively.(2)The combination forecast is of greater stability:the variance of relative error of the residual auto-regressive model,the Kalman filtering algorithm and the combination model are 0.002,0.007 and 0.001,respectively.(3)Provided that other conditions are invariant,the predicted value of gas consumption in 2018 is 241.81×10~9 m^3.Compared to other time-series forecasting methods,this combined model is less restrictive,performs well and the result is more credible.

Generalized Weighted Mean Combining Forecasting and Its Application in the Forecasting of Air Materials Consumption

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.

The Forecast and Regional Analysis of the Private Motor Vehicle Market in China

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.