Consumption Forecast Of Domestic Mineral Resources Based On Combinational Model

As the basis for a non-renewable materials and important strategic, and consumption of copper is second only to aluminum,China has become the world's largest consumer of copper in the world,However,both highly demand of copper and its shortage has restricted the economic development of China. Although China imports a huge amount of copper every year,China plays less important role in the world copper market.Therefore,the steady and rapid development of the domestic industry of copper great challenges. With respect to predicting consumption in China faces of copper,accurately, it is the basis to make the right decisions for Mineral resource managers. Market information is various and complex, which often makes people helpless,and difficult to make decisions on the market.Therefore ,it is very urgent to know how to predict consumption of copper accurately.However,at current China's consumption of copper forecasting exists problems such as the wrong trend of forecasting,the low accuracy of prediction,the main research in this area remain in the qualitative level,the forecasting results are not very satisfactory, many of the best forecasting system have not yet been applied to this area, the paper is supported by Sichuan Province of the project ,Consumption of mainmineral resources prediction and its application issues, it aims to apply some relatively mature models to consumption of Copper forecasting and to getbetter precision of consumption of Copper forecasting.The paper firstly studied the meanings and characteristics of the predicting, pointed out the characteristics of consumption of Copper forecasting, on the basis, reviewed the forecasting methods which commonly used and their characteristics,such as regression analysis forecasting model, exponential smoothing forecasting model,qualitative analysis method and BP neural network model,analyzed the advantages and disadvantages of these models, explained the rationality and feasibility of combinational forecasting model,and proposed several design ideas and modeling steps for the combinational model.When modeling,the paper used a single forecasting model for the training firstly,themodel parameters were optimized through a variety of methods, the model configuration was optimal,in order to get better outcome. When conducting combined forecasts,the model was divided into two parts-factors prediction and the results prediction,in order to combine the advantages of sevral forecasting models to achieve the purpose of improving predicting accuracy。In the part of the factors predicting,single forecasting model was used to train and forecast the factors which affect consumption of Copper. The predicting results of this part was the input of the results prediction. The regression neural networks prediction model and GM(1,N)model was used as the model of the results prediction. After training,we got the outcome .Six combined forecasting model are as follow: ANN+ANN, TS+ ANN ,ANN+GM(1,N),GM(1,1)+GM(1,N),TS+GM(1,N) three single-forecasting models are: neural network prediction model(ANN), gray system prediction model(GM(1,1)), time series triple exponential smoothing forecasting model(TS).When finished modeling, the paper used the data of consumption of domestic copper ,as well as its related factors, as input to train the prediction models. In the training process,I selected the model parameters by constantly training to get the best result. After got the result,this paper compared the results by the RMSE error evaluation criteria.Through the above work,the following are conclusions:(1) In the single forecasting models,the gray system model is not satisfactory in the long-term prediction,it is suitable for the short-term prediction.The time series triple exponential smoothing are better to forecast the trend of the future data than others.(2) In all the combinational forecasting models,the RMSE value of the GM(1,1)+ANN model is minimum,forecasting result is the best. But the predicted results of the combinational forecasting model are not better than a single prediction model always. The ANN+ANN prediction model is the worst combinational model. The reason may be that the relevance of the two systems is greater,so when designing the combinational model,we should consider to choose two models with a low correlation.However,in this paper,there are still many deficiencies and aspects for improvement, for example, Less data, the combinational forecasting models were limited to a simple series connection which can not integrate the advantages of both models fully, we should do more research on the combinational methods in the future.In addition,the paper used multi-test algorithm for the model parameter optimization,which is less scientific and rigorous.Moreover,the theory of the error amendments is not mature and needs much further study.