The grey forecasting model has been successfully applied to many fields. However, the precision of GM(1,1) model is not high. In order to remove the seasonal fluctuations in monitoring series before building GM(1,1) m...The grey forecasting model has been successfully applied to many fields. However, the precision of GM(1,1) model is not high. In order to remove the seasonal fluctuations in monitoring series before building GM(1,1) model, the forecasting series of GM(1,1) was built, and an inverse process was used to resume the seasonal fluctuations. Two deseasonalization methods were presented , i.e., seasonal index-based deseasonalization and standard normal distribution-based deseasonalization. They were combined with the GM(1,1) model to form hybrid grey models. A simple but practical method to further improve the forecasting results was also suggested. For comparison, a conventional periodic function model was investigated. The concept and algorithms were tested with four years monthly monitoring data. The results show that on the whole the seasonal index-GM(1,1) model outperform the conventional periodic function model and the conventional periodic function model outperform the SND-GM(1,1) model. The mean Absolute error and mean square error of seasonal index-GM(1,1) are 30.69% and 54.53% smaller than that of conventional periodic function model, respectively. The high accuracy, straightforward and easy implementation natures of the proposed hybrid seasonal index-grey model make it a powerful analysis technique for seasonal monitoring series.展开更多
Analyzing time series characteristics of red tide is the basis of disaster prevention and mitigation,which is very important to red tide prediction.There are trend comp onents and periodic components in annual time se...Analyzing time series characteristics of red tide is the basis of disaster prevention and mitigation,which is very important to red tide prediction.There are trend comp onents and periodic components in annual time series of occurrence freque ncy and area of red tides,so Gray-Periodic Extensional Combinatorial Model(GPECM)is used to extract these components.The fitting degree of occurrence frequency and area can reach 95.20% and 95.24%,respectively.The performance of GPECM is better than Gray Model,Fourier Series Extension Model,and Holt-Winter Exponential Smoothing Model in model stability.Consequently,it is used to forecast the occurrence frequency and area in 2020 and 2021,and results show that the annual frequency of red tides in 2020 and 2021 can rise to 39 and 41,respectively,and that the annual occurrence area of red tides can rise to 3168 km^(2),which is about 59% more than last year.In 2021,it can fall to 1901 km^(2).展开更多
Thinking of grey group model is the improvement on the traditional grey model. It does not merely use a grey model as the ultimate basis, but takes full account of the traditional GM （1,1） model and the GM （1, n） ...Thinking of grey group model is the improvement on the traditional grey model. It does not merely use a grey model as the ultimate basis, but takes full account of the traditional GM （1,1） model and the GM （1, n） model and join two predictions to form a prediction interval. So, the results are more reasonable and more realistic requirements and have strong guidance and reference. The farther the forecast period is, the worse the forecast is. The forecasts in the forecast period of 1-3 Years are the best, but the results of long-term are only as a reference value and the guidance data. Therefore, as the forecast period goes on, rolling grey model is used to increase accuracy.展开更多
文摘The grey forecasting model has been successfully applied to many fields. However, the precision of GM(1,1) model is not high. In order to remove the seasonal fluctuations in monitoring series before building GM(1,1) model, the forecasting series of GM(1,1) was built, and an inverse process was used to resume the seasonal fluctuations. Two deseasonalization methods were presented , i.e., seasonal index-based deseasonalization and standard normal distribution-based deseasonalization. They were combined with the GM(1,1) model to form hybrid grey models. A simple but practical method to further improve the forecasting results was also suggested. For comparison, a conventional periodic function model was investigated. The concept and algorithms were tested with four years monthly monitoring data. The results show that on the whole the seasonal index-GM(1,1) model outperform the conventional periodic function model and the conventional periodic function model outperform the SND-GM(1,1) model. The mean Absolute error and mean square error of seasonal index-GM(1,1) are 30.69% and 54.53% smaller than that of conventional periodic function model, respectively. The high accuracy, straightforward and easy implementation natures of the proposed hybrid seasonal index-grey model make it a powerful analysis technique for seasonal monitoring series.
文摘Analyzing time series characteristics of red tide is the basis of disaster prevention and mitigation,which is very important to red tide prediction.There are trend comp onents and periodic components in annual time series of occurrence freque ncy and area of red tides,so Gray-Periodic Extensional Combinatorial Model(GPECM)is used to extract these components.The fitting degree of occurrence frequency and area can reach 95.20% and 95.24%,respectively.The performance of GPECM is better than Gray Model,Fourier Series Extension Model,and Holt-Winter Exponential Smoothing Model in model stability.Consequently,it is used to forecast the occurrence frequency and area in 2020 and 2021,and results show that the annual frequency of red tides in 2020 and 2021 can rise to 39 and 41,respectively,and that the annual occurrence area of red tides can rise to 3168 km^(2),which is about 59% more than last year.In 2021,it can fall to 1901 km^(2).
文摘Thinking of grey group model is the improvement on the traditional grey model. It does not merely use a grey model as the ultimate basis, but takes full account of the traditional GM （1,1） model and the GM （1, n） model and join two predictions to form a prediction interval. So, the results are more reasonable and more realistic requirements and have strong guidance and reference. The farther the forecast period is, the worse the forecast is. The forecasts in the forecast period of 1-3 Years are the best, but the results of long-term are only as a reference value and the guidance data. Therefore, as the forecast period goes on, rolling grey model is used to increase accuracy.
