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首页> 外文期刊>International journal of production economics >A hybrid seasonal autoregressive integrated moving average and quantile regression for daily food sales forecasting
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A hybrid seasonal autoregressive integrated moving average and quantile regression for daily food sales forecasting

机译：混合季节性自回归综合移动平均和分位数回归用于每日食品销售预测

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In the retail stage of a food supply chain, food waste and stock-outs occur mainly due to inaccurate forecasting of sales which leads to incorrect ordering of products. The time series sales in food retail industry are characterized by high volatility and skewness, which vary by time. So, the interval forecasts are required by the retail companies to set appropriate inventory policy (reorder point or safety stock level). This paper attempts to develop a seasonal autoregressive integrated moving average with external variables (SARIMAX) model to forecast daily sales of a perishable food. The process of fitting a SARIMAX model in this study involves: (i) the development of Seasonal Autoregressive Integrated Moving Average (SARIMA) model and (ii) combining the SARIMA model and the demand influencing factors using linear regression. As the SARIMAX using multiple linear regression (SARIMA-MLR) model produces only mean forecast, the possibility of underestimation and overestimation is very high due to high service level, peak, and sparse sales in food retail industry. Therefore, a hybrid SARIMA and Quantile Regression (SARIMA-QR) is developed to construct high and low quantile predictions. Instead of extrapolating the quantiles from the mean point forecasts of SARIMA-MLR model based on the assumption of normality, the SARIMA-QR model directly forecasts the quantiles. The developed SARIMA-MLR and SARIMA-QR models are applied in modeling and forecasting of sales data, i.e., the daily sales of banana from a discount retail store in Lower Bavaria, Germany. The results show that the SARIMA-MLR and -QR models yield better forecasts at out-sample data when compared to seasonal naive forecasting, traditional SARIMA, and multi-layered perceptron neural network (MLPNN) models. Unlike the SARIMA-MLR model, the SARIMA-QR model provides better prediction intervals and a deep insight into the effects of demand influencing factors for different quantiles. (C) 2015 Elsevier B.V. All rights reserved.
机译：在食品供应链的零售阶段，发生食品浪费和缺货的主要原因是对销售的预测不正确，从而导致产品订购不正确。食品零售行业中的时间序列销售具有高波动性和偏斜性的特点，随时间变化。因此，零售公司需要间隔预测来设置适当的库存策略（重新订购点或安全库存水平）。本文尝试建立带有外部变量的季节性自回归综合移动平均值（SARIMAX）模型，以预测易腐食品的日销售量。在此研究中，拟合SARIMAX模型的过程涉及：（i）季节性自回归综合移动平均线（SARIMA）模型的开发，以及（ii）使用线性回归将SARIMA模型和需求影响因素结合在一起。由于使用多元线性回归（SARIMA-MLR）模型的SARIMAX仅产生均值预测，因此由于食品零售行业的服务水平高，销售高峰和销售稀少，低估和高估的可能性非常高。因此，开发了混合SARIMA和分位数回归（SARIMA-QR）来构建高分位数和低分位数的预测。 SARIMA-QR模型不是直接基于正态假设从SARIMA-MLR模型的均值预测中推断分位数，而是直接预测分位数。开发的SARIMA-MLR和SARIMA-QR模型用于销售数据的建模和预测，即从德国下巴伐利亚州的一家折扣零售店购买香蕉的日销售量。结果表明，与季节性天真预测，传统SARIMA和多层感知器神经网络（MLPNN）模型相比，SARIMA-MLR和-QR模型在样本外数据上产生更好的预测。与SARIMA-MLR模型不同，SARIMA-QR模型提供了更好的预测间隔，并深入了解了不同分位数的需求影响因素的影响。 （C）2015 Elsevier B.V.保留所有权利。

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