Prediction of Air Cargo Volume Based on Grey-periodic Extensional Combinatorial Model
Abstract
Aviation logistics volume changes over time is an obvious regularity. According to the characteristics of aviation logistics volume data, on the basis of common GM(1,1) model and by means of establishing residual error periodic extensional model, the paper extracts predominant period and reconstruct a new data sequence, it can well overcome the contradiction between the growth tendency and the periodic fluctuation of seasonal products sales. Applying the method, a typical simulation study was carried out to illustrate its validity. Is is found that the model can greatly increased the accuracy of aviation logistics volume predication
Keywords
Aviation logistics, Grey theory, Gray-periodic extensional combinatorial model
DOI
10.12783/dtetr/acaai2020/34198
10.12783/dtetr/acaai2020/34198
Refbacks
- There are currently no refbacks.