[4D1-OS-33a] OS-33

[4D1-OS-33a-02] Zero-shot Demand Forecasting for Products with Limited Sales Periods

〇Shota Nagai¹, Ryota Inaba¹, Rei Oishi¹, Shuhei Aikawa¹, Yusuke Mibuchi¹, Hinata Moriyama¹, Ken Kobayashi¹, Kazuhide Nakata¹ (1. Institute of Science Tokyo)

Keywords:Demand forecasting, time-series forecasting

Demand forecasting is an essential task in retail and manufacturing industries and has been the subject of numerous studies. Conventional popular time-series forecasting methods, such as the ARIMA model, require us to develop a forecasting model for each product.However, when products are frequently replaced and have short sales periods, we do not have enough data to build models individually.This study focuses on zero-shot time-series forecasting methods for demand forecasting with limited data. Zero-shot time-series forecasting is a framework for time-series prediction that does not require fine-tuning with specific time-series data to be predicted.To address the data shortage in practical situations, we propose a zero-shot demand forecasting model that considers exogenous variables. Our experiments with real data demonstrate that our proposed method achieved higher prediction accuracy than existing time-series forecasting methods, especially for products with short sales periods.