论文标题
关于预测零售的比较研究
A Comparative Study on Forecasting of Retail Sales
论文作者
论文摘要
考虑到趋势，季节性，事件以及诸如市场竞争，客户偏好的变化或不可预见的事件等趋势，季节性，事件以及未知的事件等趋势的性质，预测大型零售公司的产品销售是一项具有挑战性的任务。在本文中，我们基于沃尔玛的历史销售数据进行基准预测模型，以预测其未来的销售。我们提供了全面的理论概述和对最新时间表预测模型的分析。然后，我们将这些模型应用于预测挑战数据集（Kaggle的M5预测）。具体来说，我们使用传统模型，即Arima（自回归的集成移动平均线），并最近开发了高级模型，例如由Facebook开发的先知模型，由Microsoft和Benchmark和Benchmark Marks开发的。结果表明，Arima模型的表现优于Facebook先知和LightGBM模型，而LightGBM模型在预测准确性方面可以忽略不计，可为大数据集获得巨大的计算增益。
Predicting product sales of large retail companies is a challenging task considering volatile nature of trends, seasonalities, events as well as unknown factors such as market competitions, change in customer's preferences, or unforeseen events, e.g., COVID-19 outbreak. In this paper, we benchmark forecasting models on historical sales data from Walmart to predict their future sales. We provide a comprehensive theoretical overview and analysis of the state-of-the-art timeseries forecasting models. Then, we apply these models on the forecasting challenge dataset (M5 forecasting by Kaggle). Specifically, we use a traditional model, namely, ARIMA (Autoregressive Integrated Moving Average), and recently developed advanced models e.g., Prophet model developed by Facebook, light gradient boosting machine (LightGBM) model developed by Microsoft and benchmark their performances. Results suggest that ARIMA model outperforms the Facebook Prophet and LightGBM model while the LightGBM model achieves huge computational gain for the large dataset with negligible compromise in the prediction accuracy.
