论文标题
对销售预测的概率时间序列模型的令人担忧的分析
A Worrying Analysis of Probabilistic Time-series Models for Sales Forecasting
论文作者
论文摘要
概率时间序列模型在预测领域变得很流行，因为它们有助于在不确定性下做出最佳决策。尽管兴趣越来越大，但缺乏彻底的分析阻碍了选择值得申请所需任务的方法。在本文中，我们分析了三种销售预测的三种突出的概率时间序列模型的性能。为了消除随机机会在体系结构的性能中的作用，我们制定了两个实验原则。 1）具有各种交叉验证集的大规模数据集。 2）标准化的培训和超参数选择。实验结果表明，一个简单的多层感知器和线性回归在没有任何功能工程的情况下优于RMSE上的概率模型。总体而言，概率模型无法比相当简单的基准在点估计（例如RMSE和MAPE）上实现更好的性能。我们分析和讨论概率时间序列模型的性能。
Probabilistic time-series models become popular in the forecasting field as they help to make optimal decisions under uncertainty. Despite the growing interest, a lack of thorough analysis hinders choosing what is worth applying for the desired task. In this paper, we analyze the performance of three prominent probabilistic time-series models for sales forecasting. To remove the role of random chance in architecture's performance, we make two experimental principles; 1) Large-scale dataset with various cross-validation sets. 2) A standardized training and hyperparameter selection. The experimental results show that a simple Multi-layer Perceptron and Linear Regression outperform the probabilistic models on RMSE without any feature engineering. Overall, the probabilistic models fail to achieve better performance on point estimation, such as RMSE and MAPE, than comparably simple baselines. We analyze and discuss the performances of probabilistic time-series models.
