数据驱动物流中时间序列预测的统计与机器学习方法比较——一项模拟研究
Comparing statistical and machine learning methods for time series forecasting in data-driven logistics -- A simulation study
AI总结：
本文通过模拟线性和非线性时间序列，系统比较统计学习方法与机器学习方法在物流场景中的一步预测性能，为数据驱动物流预测方法选择提供依据。
摘要：
物流与供应链管理中的许多规划和决策活动都基于对多个时间依赖因素的预测。因此，规划质量取决于预测质量。我们在一组广泛的模拟时间序列上，从开箱即用的预测性能角度比较了各种预测方法。我们模拟了各种线性和非线性时间序列，并考察了统计学习方法的一步预测性能。
英文摘要：
Many planning and decision activities in logistics and supply chain management are based on forecasts of multiple time dependent factors. Therefore, the quality of planning depends on the quality of the forecasts. We compare various forecasting methods in terms of out of the box forecasting performance on a broad set of simulated time series. We simulate various linear and non-linear time series and look at the one step forecast performance of statistical learning methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
