Terminal Research And Application Of Prediction Algorithm

On the terminals, especially the large scale one, route is particularly numerous, the data changes rapidly, as well as frequent Transformation and complicated business scenes. In the actual operations, to guarantee high efficiency and abundant output, it need frequently estimate kinds of business so as to make related strategies. However, it is impossible to get these estimate data directly. So far, most of the wharfs in the world, the main systems are designed for the operation and resource distribution, which didn't have technical systems to supply such kind of prediction data, In that case the user have to face the embarrassment that they are difficult to get the useful part from a large amount of data. To solve this problem, it must create a special system to apply to different kinds of prediction data for decision making. In the paper research, it found that terminal business data have the following characters: 1) the input data disperse in different systems; 2) output data are the knowledge according to the interaction of input data; 3) the main business styles are based on prediction, etc.According to these characters, this paper Proposes a methodology which using data to maintain conception deeply to create prediction model, constructing customized system to supply data support for decision making.This paper analyses the background and objectives of the system, as well as business requirements, users'demand and character requirements. Following these requirements, It comes the design of system and database. This paper compared and studies different kinds of the common data mining algorithms, especially focus on forecast algorithms. Base on these studies, it confirmed that using grey model is finally quite applicable to system's prediction algorithm, and verified the accuracy of the algorithm. The result shows that grey model is compatible for terminal data characters and prediction.In view of that the disadvantage of grey prediction algorithm precision will gradually degrade with the prediction scope enlarging; This paper studies the other researchers'reparation method for this disadvantage, and combined with the terminal business data's characters to analyze, raise a low complexity and cost solution, which it's a dynamic re-modeling and self suitable method. Test and application verified that it's a feasible method.All system test results and user feedback proved that system performance and response speed was satisfied, the data precision met the actual application, it was convenient and fast to show the prediction result to user, which has helped to make decision, reduced the users'awkwardness and improving the operating efficiency .Various targets met the demand and the usage , which achieved the goal of this paper.