论文标题
用于块状和间歇性需求预测的新指标：面向股票的预测成本
A New Metric for Lumpy and Intermittent Demand Forecasts: Stock-keeping-oriented Prediction Error Costs
论文作者
论文摘要
产品需求的预测对于物流和生产的短期和长期优化至关重要。因此，最准确的预测是可取的。为了最佳地训练预测模型，与实际需求相比，预测的偏差需要由适当的度量评估。但是，如果公制不代表实际的预测误差，则预测模型的优化不足，因此将产生不准确的预测。但是，最常见的指标（例如MAPE或RMSE）不适用于评估预测错误，尤其是对于块状和间歇性的需求模式，因为它们不充分考虑了时间变化（例如，在实际需求之前或之后的预测）或成本关联的方面。因此，我们提出了一个新颖的指标，除了统计考虑之外，还解决了业务方面。此外，我们根据汽车售后市场的模拟和真实需求时间序列评估了度量。
Forecasts of product demand are essential for short- and long-term optimization of logistics and production. Thus, the most accurate prediction possible is desirable. In order to optimally train predictive models, the deviation of the forecast compared to the actual demand needs to be assessed by a proper metric. However, if a metric does not represent the actual prediction error, predictive models are insufficiently optimized and, consequently, will yield inaccurate predictions. The most common metrics such as MAPE or RMSE, however, are not suitable for the evaluation of forecasting errors, especially for lumpy and intermittent demand patterns, as they do not sufficiently account for, e.g., temporal shifts (prediction before or after actual demand) or cost-related aspects. Therefore, we propose a novel metric that, in addition to statistical considerations, also addresses business aspects. Additionally, we evaluate the metric based on simulated and real demand time series from the automotive aftermarket.
