Ye, L. and Xie, N. and Boylan, J.E. and Shang, Z. (2024) Forecasting seasonal demand for retail : A Fourier time-varying grey model. International Journal of Forecasting. ISSN 0169-2070 (In Press)
Full text not available from this repository.Abstract
Seasonal demand forecasting is critical for effective supply chain management. However, conventional forecasting methods face difficulties accurately estimating seasonal variations, owing to time-varying demand trends and limited data availability. In this paper, we propose a Fourier time-varying grey model (FTGM) to tackle this issue. The FTGM builds upon grey models, which are effective with limited data, and leverages Fourier functions to approximate time-varying parameters that allow it to represent seasonal variations. A data-driven selection algorithm adaptively determines the appropriate Fourier order of the FTGM without prior knowledge of data characteristics. Using the well-known M5 competition data, we compare our model with state-of-the-art forecasting methods taken from grey models, statistical methods, and architectures of neural network-based methods. The experimental results show that the FTGM outperforms popular seasonal forecasting methods in terms of standard accuracy metrics, providing a competitive alternative for seasonal demand forecasting in retail companies.