一种新的块状和间歇性需求预测指标：面向库存的预测误差成本
A New Metric for Lumpy and Intermittent Demand Forecasts: Stock-keeping-oriented Prediction Error Costs
摘要：
产品需求预测对于物流和生产的短期和长期优化至关重要。因此，最准确的预测可能是可取的。为了最佳地训练预测模型，需要通过适当的度量来评估预测与实际需求相比的偏差。然而，如果度量不代表实际的预测误差，则预测模型未被充分优化，并且因此将产生不准确的预测。然而，诸如MAPE或RMSE的最常见的度量不适合于预测误差的评估，特别是对于不稳定和间歇性需求模式，因为它们不足以解释例如，时间变化（在实际需求之前或之后的预测）或成本相关方面。因此，我们提出了一个新的指标，除了统计考虑，还解决了业务方面。此外，我们根据汽车售后市场的模拟和实时需求时间序列评估该指标。
英文摘要：
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.
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。
