Title of Paper:Seasonal mixing grey prediction model and its application under the collaboration of multiple frequency information

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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:Control and Decision

Place of Publication:沈阳

Funded by:国家自然科学基金项目 (72071023,72001107,72271120); 江苏省研究生科研与实践创新计划项目 (KYCX24_0521)

Key Words:SMFGM(1, N) model; grey correlation degree model of frequency mixing; Nakagami function; seasonal data; multiple frequency information; tax revenue forecast

Abstract:When modeling with the collaborative use of multi-frequency information, the issues of inconsistent variable frequencies and the seasonal effects often associated with high-frequency variables arise. To address these, this paper proposes a Seasonal Mixed-Frequency Grey Prediction Model (SMFGM(1,N)). The new model aligns variable frequencies by introducing the Nakagami function, eliminates the seasonal effects of variables based on seasonal factors, and incorporates nonlinear terms to capture the nonlinear impacts of time on the system. Additionally, to identify the lag parameters in the new model, the Nakagami function is combined with the classical grey relational model to propose a Mixed-Frequency Grey Relational Model, which helps identify the relationships between variables with different frequencies. Finally, using a case study of annual GDP and quarterly tax revenue, the new model is compared with the mixed-frequency data sampling model, other grey prediction models, neural network models, and statistical models. The results demonstrate that the SMFGM(1,N) model has superior modeling performance and effectively addresses the prediction problems of mixed-frequency data with seasonal patterns, providing a new method for modeling multi-frequency information systems.

Discipline:Management Science

Document Type:J

Translation or Not:no

Included Journals:EI

Co-author:Wang Junjie,Zeng Bo

Correspondence Author:Mi Chuanmin