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Study of Economic Forecasting in Harbour Engineering Project

Author: WangLin
Tutor: WangYuanZhan
School: Tianjin University
Course: Port, Coastal and Offshore Engineering
Keywords: BP neural network Genetic Algorithms Gray model multivariate linear model Combination Forecasting Random number Cargo throughput
CLC: F550
Type: Master's thesis
Year: 2003
Downloads: 333
Quote: 10
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The core of the management decision-making , scientific prediction is the guarantee of the right decisions . Economic forecasts are widely used in socio-economic sectors , in planning the future development strategy or investment projects need more accurate grasp of the future economic development of the situation , the direction for decision-makers . Port project accurately predict the future development of the port throughput level , is the key to determine the port construction scale . This paper, on the basis of the basic concepts of economic forecasts , three independent economic forecasting model , and a combination forecasting model is given . The basic concept of neural networks , genetic algorithms , gray theory and linear regression analysis , the computing process the ( modeling method ) , test indicators . Proposed the improved method of the neural networks and genetic algorithms and neural network combined to improve the performance of the model . Gray model of the defect in the modeling process , the GM (1,1) model to improve the accuracy of model fitting . The most commonly used linear regression models , the modeling process is given , and focus on model checking and correction method . The three models from different angles of the relevant economic variables to predict , from the integrity of the information considered one-sidedness , the paper also gives the combination forecasting model . The paper describes in detail the optimum combination of the presence of judgment and the screening of non-negative constraints , given the constant weight combination forecasting model quadratic programming algorithm . Tianjin Port , for instance , to study the development of its cargo throughput from a different angle , each individual model fitting accuracy to BP neural network model based on genetic algorithms , multiple linear regression model , followed by the lowest of gray model accuracy . However, the combination of the three predictive model accuracy higher than all of the individual model , which also shows the combination forecasting model from multiple perspectives , consider the full range of various factors , determine the theoretical value more fit actual , even with low accuracy the models also improve the overall accuracy of the combined forecasting model .

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CLC: > Economic > Transportation and economic > Sea transport and economic > Waterway transport economic theory
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