Title of Paper:A novel seasonal discrete grey forecasting model based on transverse and longitudinal dimensions and its application

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Affiliation of Author(s):南京航空航天大学经济与管理学院

Teaching and Research Group:管理科学与工程

Journal:Chinese Journal of Management Science

Key Words:seasonal discrete grey forecasting model; real domain differentiation fractional order; dual perspective of transverse and longitudinal dimensions; quarterly GDP forecasting

Abstract:Seasonal data have multiple complex characteristics such as seasonal fluctuation, cycle coherence, and stage variability, which bring challenges to the scientific construction of its forecasting models. To this end, firstly based on the matrixed processing method of time-series data, the dual perspectives of transverse and longitudinal dimensions of seasonal volatility characteristics are considered. Then, a new real domain fractional discrete grey prediction model is constructed through the introduction of dummy variables and the differential design of accumulation orders, realizing the effective simulation of the characteristics of seasonal data cycle coherence and stage variability. And a particle swarm algorithm is used to synchronize and optimize each variable of the new model in the real domain to further improve the modeling performance of the new model. Also, the modeling by two literature cases shows that the new model error decreases by about 77% and 82%, respectively, compared with the comparative literature models. Finally, the new model is applied to solve the forecasting problem of China's quarterly GDP data, and the results show that the error of the new model is 0.813%, while others are 2.545%, 1.517% and 2.667%, respectively. The research results provide a new modeling tool for studying the forecasting problem of seasonal data, which is of positive significance for improving and enriching the grey forecasting model method system.

Translation or Not:no

Co-author:Zeng Bo,Li Mingzhu,徐盈婷

Correspondence Author:Mi Chuanmin