论文标题
使用机器学习预测零售产品的未来销售
Predicting Future Sales of Retail Products using Machine Learning
论文作者
论文摘要
基于当前和过去数据做出未来预测的技术一直是在各种现实生活中直接应用的领域。我们正在讨论本文类似的问题。问题声明由Kaggle提供，Kaggle也是Kaggle平台上正在进行的竞争。在这个项目中，我们与一个充满挑战的时间序列数据集合作，该数据集由每日销售数据组成，该数据由最大的俄罗斯软件公司之一-1C公司提供。目的是预测下个月给定数据的每种产品的总销售额，并存储。 为了进行下个月的预测，我们已经部署了极端梯度提升（XGBOOST）和基于短期内存（LSTM）的网络体系结构来执行学习任务。实际的和预测的目标值之间的根平方误差（RMSE）用于评估性能，并在部署的算法之间进行比较。已经发现，Xgboost在此数据集上的表现要比LSTM好，这可以归因于其相对较高的稀疏性。
Techniques for making future predictions based upon the present and past data, has always been an area with direct application to various real life problems. We are discussing a similar problem in this paper. The problem statement is provided by Kaggle, which also serves as an ongoing competition on the Kaggle platform. In this project, we worked with a challenging time-series dataset consisting of daily sales data, kindly provided by one of the largest Russian software firms - 1C Company. The objective is to predict the total sales for every product and store in the next month given the past data. In order to perform forecasting for next month, we have deployed eXtreme Gradient Boosting (XGBoost) and Long Short Term Memory (LSTM) based network architecture to perform learning task. Root mean squared error (RMSE) between the actual and predicted target values is used to evaluate the performance, and make comparisons between the deployed algorithms. It has been found that XGBoost fared better than LSTM over this dataset which can be attributed to its relatively higher sparsity.
