Analysis And Prediction For Chrysalis Silk Price Index Based On Grey System Theory

The study of silk industry at home and abroad all pays much attention to datacollection. People collect data of output, price and export to analyze and acquire marketinformation. But now most of the data study focuses on quantitative analysis based onthe existing data, making the study lagged behind in the guidance of the marketjudgment, not forward-looking or prediction. Therefore, establishing appropriatemathematical models based on the known information to forecast and study silk marketrule is very important.Using actual data operated in market as the research background, the paper appliesthe grey system theory method and grey model of prediction to the fluctuation analysisof Chrysalis silk market. It sets actual data related as the object; respectively buildGM (1,1)model, the improved GM (1,1)model and Grey-Markov model, while usingMATLAB tools to realize the data visualization processing through programming.Based on this analysis, it also studies, analyzes and predicts chrysalis silk price indexfrom different angles and analyzes, compares and evaluates the results. It uses drychrysalis price index as the research object, respectively establishes the GM (1,1)modeland the improved GM (1,1)model to forecast and analyze, displaying that both can beused for price index short term prediction. At the same time, it shows the advantages ofthe improved GM (1,1)model in prediction price index through empirical comparison.Based on the improved GM (1,1)model, it studies respectively of the prediction ofchrysalis silk price index and silk price index, discusses the difference rule of variousindex prediction accuracy in the price index system. In the light of fluctuationcharacteristic of chrysalis silk price index data, the paper establishes Grey-Markovmodel to forecast the chrysalis silk price index and silk price index, verifying the feasibility of the application of Grey-Markov model. At last, it studies the prediction ofsilk fabric price index using the BP neural network model, the improvedGM (1,1)model and Grey-Markov model and also compares the three methods, showingthat the Grey-Markov model does better in prediction. In a word, all of these methodscan be used in short term prediction of chrysalis silk price index. And the prediction andresearch for real silk market has certain realistic directive significance.