Research On Forecast Of Contracts For Shipbuilding Of China,Japan And Korea Based On Grey-Neural Network Combined Model

Shipping industry is a heavy asset industry.The value of ship assets is significant.Shipbuilding industry is the foundation of shipping industry.China,Japan and Korea occupy the maj ority of the world shipbuilding market share and China,Japan and Korea are the focus of the research of world shipbuilding industry.However,at present,the quantitative research in this field mostly adopts the traditional economic research methods.In recent years,in computer science field,data mining and machine learning technology have developed rapidly and their application fields are also expanding.Therefore,this paper will use machine learning algorithm to analyze the shipbuilding industry of China,Japan and South Korea,and explore the shipbuilding new contracts and orderbook of China,Japan and South Korea analysis and prediction model.This paper chooses China,Japan and South Korea as the research objects.Firstly,it expounds the current shipbuilding background and current research situation in shipbuilding field,and puts forward the purpose and significance of this paper.In this paper,shipbuilding orderbook are selected as the forecasting target.According to the economic theory and the actual market background,eight influencing indicators were choosen for forecasting and modeling.They have a significant impact on orderbook in shipbuilding industry of China,Japan and South Korea.To deal with the imperfection of traditional time series forecasting methods such as grey forecasting and ARMA forecasting,which only analyze the time series rule of a single variable and neglect the impact of other indicators on the forecasting target,this paper puts forward an improved idea of using regression to predict shipbuilding orderbook.Firstly,a forecasting model based on multiple linear regression is constructed.In view of the possible non-linear problems among the indicators,an improved prediction model based on polynomial regression is proposed.As well,a multi-linear regression model based on ridge estimation is proposed to solve the possible multi-collinearity problems among the indicators.Aiming at the impact indicators of the forecasting target,the time series forecasting grey model is constructed to forecast the indicators related to shipbuilding orderbook.Then,in order to find a better prediction model for shipbuilding orderbook,a prediction model based on neural network is constructed.Then,aiming at the problem of insufficient use of grey model for indicators data set,and in order to realize the prediction of future orderbook and to improve the forecast accuracy,this paper put forward an improvement idea that combined the grey and neural network model into FGBP-SOB and GBP-SOB.In this way,the FGBP-SOB and GBP-SOB models can fully integrate the advantage of GM(1,1)and BP neural network model.And experiments prove that GBP-SOB model has better forecast accuracy than single model.After that,in order to find the competitive relationship between China,Japan and Korea,an analysis model based on Apriori algorithm is proposed to analyze the rising and falling of shipbuilding new contracts of China,Japan and Korea.Finally,this paper applies the research results to the shipping module of the China-ASEAN Ocean Big Data Platform,realizes the retrieval and prediction functions of orderbook and indicators of shipbuilding between China,Japan and Korea,and provides reference of prediction and research for-the platform.