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Sales Forecast for O2O Services - Based on Incremental Random Forest Method

机译：O2O服务的销售预测-基于增量随机森林法

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This paper proposes an incremental random forest method to forecast the sales for the O2O take-out business. The proposed method has two characteristics. First, we identify the important features that contribute most to the forecast accuracy by deleting the noisy features. This feature selection process helps to improve the forecast accuracy. Second, we use an incremental method based on random forest by adding incremental features and focus on sales increment prediction. This incremental random forest method could further help to control the forecast error. Moreover, we apply a real data set from an online merchant in one of the largest O2O platforms to validate our method. The results show that the feature selection can significantly reduce the mean absolute percentage error (MAPE) by 11.64%. Furthermore, the incremental random forest method further reduces the MAPE by 3.10%. With a better sales forecast, the merchant can improve its operations for replenishment and marketing.
机译：本文提出了一种增量随机森林法来预测O2O外卖业务的销售额。所提出的方法具有两个特征。首先，我们通过删除嘈杂的特征来确定对预测准确性影响最大的重要特征。此功能选择过程有助于提高预测准确性。其次，我们通过添加增量功能使用基于随机森林的增量方法，并专注于销售增量预测。这种增量随机森林方法可以进一步帮助控制预测误差。此外，我们在最大的O2O平台之一中应用来自在线商家的真实数据集来验证我们的方法。结果表明，特征选择可以显着降低平均绝对百分比误差（MAPE）11.64 \％。此外，增量随机森林法将MAPE进一步降低了3.10％。通过更好的销售预测，商人可以改善其业务以进行补充和营销。

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