Research on Precise Demand Prediction of New Retail Target Product Based on Dual Model

J. Xu, Zong-Shin Liu, Ying-Qi Xu, Jinpeng Hao, Jiaming Sun, Yunrui Lu, Bowen Pang
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Abstract

: In this study, the target data set is firstly obtained by data processing, and then the Grey Verhulst model and ARIMA model are respectively used for modeling and prediction research on the target data set, so as to calculate the predicted value. Then MAPE (average absolute percentage error) is calculated according to the predicted value. The typical characteristics and adaptability of Grey Verhulst model and ARIMA model are compared and analyzed. This study shows that the ARIMA model is more accurate than the Grey Verhulst model in the short term, and its prediction accuracy decreases sharply with the extension of time, which is suitable for the short term prediction. The accuracy of Grey Verhulst model is relatively stable, and the accuracy is improved with the extension of time, which is suitable for medium and long term prediction.
基于双模型的新零售目标产品需求精准预测研究
在本研究中,首先通过数据处理得到目标数据集,然后分别使用Grey Verhulst模型和ARIMA模型对目标数据集进行建模和预测研究,从而计算预测值。然后根据预测值计算平均绝对百分比误差(MAPE)。比较分析了灰色Verhulst模型和ARIMA模型的典型特征和适应性。研究表明,ARIMA模型在短期内的预测精度高于灰色Verhulst模型,随着时间的延长,其预测精度急剧下降,适合于短期预测。灰色Verhulst模型精度相对稳定,且随着时间的延长精度有所提高,适合中长期预测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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