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• W4382565370 abstract "Prediction is a kind of activity that makes use of the knowledge and means that people have already mastered to predict and judge the future development of things. Specifically, people use various qualitative and quantitative analysis methods according to the objective process and certain laws of the development and change of things in the past, and according to the state of movement and change of things, a scientific projection of the possible future trends and possible levels of things. As a kind of human cognitive activity, prediction has existed in human social practice for a long time and has been developing with the development of productivity and Relations of production. Therefore, the forecast is actually by means of the past to predict and understand the future trend of development. Usually, prediction can be divided into qualitative prediction and quantitative prediction, quantitative prediction is through the analysis of data for the prediction, often need to establish a prediction model. Gray prediction is to discover and grasp the law of system development by processing the original data and establishing the gray model and to make a scientific quantitative prediction of the future state of the system. The gray prediction model does not use the original data sequence, but the generated data sequence. Its core system is gray model, that is, the method of getting approximate exponential law from the original data by accumulative generation (or other processing generation) and then modeling. The advantage of gray prediction is that it can solve the problems of less historical data, integrity of sequence and low reliability without enough sample space of data, it can generate irregular original data to get regular generating sequence. The disadvantage is that it applies only to short-and medium-term forecasts and only to those approximating exponential growth. There should be sufficient quantitative analysis as to what model should be chosen for a particular problem. The choice of models, however, is not inflexible. A model must go through a variety of tests to determine whether it is reasonable, only through the test of the model can be used as a prediction. In this chapter, we will mainly introduce the test method of the gray prediction model, the sequence prediction, the gray catastrophe prediction, and the application example of the gray prediction in fishery science." @default.
• W4382565370 created "2023-06-30" @default.
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• W4382565370 date "2023-01-01" @default.
• W4382565370 modified "2023-09-26" @default.
• W4382565370 title "Gray Prediction" @default.
• W4382565370 cites W2313180018 @default.
• W4382565370 doi "https://doi.org/10.1007/978-981-99-0635-2_6" @default.
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