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Forecasting of GDP is considered using a novel combination forecasting model. Firstly, we establish a combination forecasting model based on three individual forecasts, in this model the individual forecasting errors can be updated over time. Secondly Ningxia per GDP from 1985 to 2010 is analyzed employing this novel combination forecasting, the empirical results show that combination forecast can...
As the largest agricultural country with a great amount of population, the cultivated land play an active role in protecting social stability and security during the process of building harmonious society, which is the material premise and necessary of humankind food-safety for our country and people lives. This paper predicted and analyzed the demand of infield in the future for Sichuan province...
In order to obtain the law of the building settlement and forecast it effectively, neural network model was established for building settlement forecasting based on measured data, and an engineering example is shown to test and verify. Firstly, data of building settlement measured were normalized; embedding dimension was selected to establish the leaning samples. Mean square error (MSE) and mean absolute...
As we all know, to predict the short-term traffic flow accurately and efficiently is the premise and key of traffic management and control. Based on these existing study, this paper selected BP neural network model in which the traffic flow difference was taken as the input parameter, applied the thought of dynamic rolling prediction to design a new short-term traffic flow prediction method, and wrote...
Short-term load forecasting is important for electricity load planning and dispatches the loading of generating units in order to meet the electricity system demand. The precision of the load forecasting is related to electricity company's economic. This paper presents a approach named an autoregressive moving average (ARMA) cooperate with BP Artificial Neural Network (BPNN) approach, which can combine...
Determining the weights in a combination forecasting is an important problem. We can translate the problem of computing weights into estimating the importance of attributes. Inclusion degree is one of the methods of computing importance of attributes. So this paper introduces a new method of computing weights based on inclusion degree. The example illustrates that the weights computed by inclusion...
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