Inaccurate forecasts of demand during promotions diminish the already meager profit margins of retailers. No forecasting method described in the literature can accurately account for the combination of seasonal sales variations and promotion-induced sales peaks over forecasting horizons of several weeks or months. We address this research gap by developing a forecasting method for seasonal, frequently promoted products that generates accurate predictions, can handle a large number of sales series, and requires minimal training data. In our method's first stage, we forecast the seasonal sales cycle by fitting a harmonic regression model to a decomposed training set, which excludes promotional and holiday sales, and then extrapolate that model to a testing set. In the second stage, we integrate the resulting seasonal forecast into a multiplicative demand function that accounts for consumer stockpiling and captures promotional and holiday sales uplifts. The final model is then fitted using ridge regression. We use sales data from a grocery retailing chain to compare the forecasting accuracy of our method with popular seasonal and promotion demand forecasting models at multiple aggregation levels for both short and long forecasting horizons. The significantly more accurate forecasts generated by our model attest to the merit of the approach developed here.

促销期间对需求的不准确预测会降低零售商本已微薄的利润率。文献中描述的任何预测方法都无法准确地说明在几周或几个月的预测范围内季节性销售变化和促销引起的销售高峰的组合。我们通过为季节性、频繁促销的产品开发一种预测方法来解决这一研究空白，该方法可以生成准确的预测，可以处理大量的销售系列，并且需要最少的训练数据。在我们方法的第一阶段，我们通过将谐波回归模型拟合到分解的训练集（不包括促销和假日销售）来预​​测季节性销售周期，然后将该模型外推到测试集。在第二阶段，我们将由此产生的季节性预测整合到一个乘法需求函数中，该函数解释了消费者库存并捕捉促销和假日销售的增长。然后使用岭回归拟合最终模型。我们使用来自杂货零售连锁店的销售数据，将我们的方法的预测准确性与短期和长期预测范围的多个聚合级别的流行季节性和促销需求预测模型进行比较。我们的模型生成的更准确的预测证明了这里开发的方法的优点。我们使用来自杂货零售连锁店的销售数据，将我们的方法的预测准确性与短期和长期预测范围的多个聚合级别的流行季节性和促销需求预测模型进行比较。我们的模型生成的更准确的预测证明了这里开发的方法的优点。我们使用来自杂货零售连锁店的销售数据，将我们的方法的预测准确性与短期和长期预测范围的多个聚合级别的流行季节性和促销需求预测模型进行比较。我们的模型生成的更准确的预测证明了这里开发的方法的优点。