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首页> 外文期刊>International journal of production economics >Forecasting of customer demands for production planning by local k-nearest neighbor models
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Forecasting of customer demands for production planning by local k-nearest neighbor models

机译：本地K最近邻模型生产规划的客户需求预测

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摘要

Demand forecasting is of major importance for manufacturing companies since it provides a basis for production planning. However, demand forecasting can be a difficult task because customer demands often fluctuate due to several influences. Methods of nonlinear dynamics have shown promising results in numerous applications but they have mostly been neglected in the context of demand forecasting. This paper evaluates the forecasting performance of local k-nearest neighbor models, which base on the theory of dynamical systems, in a comprehensive empirical study utilizing a large dataset of industrial time series of the M3Competition. After a broad literature review, the theoretical background is described. Subsequently, different parameter configurations and model selection strategies are compared. A locally constant mean and a locally constant median are compared to locally linear regression models with four different regularization methods and different parameter configurations. In this comparison, the locally constant mean and the locally linear ridge regression with high regularization parameters provide the best trade-offs between forecast accuracy and computation times. Finally, these models achieve a high performance regarding low forecast errors, short computation times as well as high service levels in an inventory simulation compared to established benchmark methods. In particular, they obtain the best results among all applied methods regarding short time series. Moreover, they achieve the lowest errors considering the original accuracy criterion of the M3-Competition. Hence, local k-nearest neighbor models can be regarded as a valid alternative for demand forecasting in an industrial context, accomplishing high forecast accuracy with short computation times.
机译：随着生产规划的基础，需求预测对于制造公司来说是一个重大重要性。然而，需求预测可能是一项艰巨的任务，因为客户要求由于几个影响而往往波动。非线性动力学的方法显示了许多应用中的有希望的结果，但在需求预测的背景下大多被忽略了。本文评估了本地K-最近邻模型的预测性能，该模型基于动态系统理论，在综合实证研究中利用M3耐级的大型工业时间序列进行了大型数据集。在广泛的文献综述之后，描述了理论背景。随后，比较了不同的参数配置和模型选择策略。将局部恒定的均值和局部恒定的中值与具有四种不同正则化方法和不同参数配置的局部线性回归模型进行比较。在这种比较中，具有高正则化参数的局部恒定均值和局部线性脊回归在预测精度和计算时间之间提供了最佳权衡。最后，与建立的基准方法相比，这些模型在库存模拟中实现了对低预测误差，短计算时间以及高服务水平的高性能。特别是，它们在所有应用方法中获得了关于短时间序列的最佳结果。此外，考虑到M3竞争的原始准确性标准，它们达到了最低误差。因此，本地K-最近邻型模型可以被视为在工业背景中的需求预测的有效替代方案，实现具有短的计算时间的高预测精度。

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