融合时空与生命周期因素，用在线数据提升汽车需求预测精度
Automobile demand forecasting: Spatiotemporal and hierarchical modeling, life cycle dynamics, and user-generated online information
- 构建多层级、多市场的时空联合预测模型，整合轻量梯度提升与分位数回归
- 在线行为数据使细分品类预测误差降低12.3%，整数化处理显著提升可操作性
- 适合车企战略规划与供应链管理团队参考，尤其关注市场波动应对
高端汽车制造商面临产品种类繁多、细分车型数据稀疏及市场波动加剧的复杂预测挑战。本研究基于德国某豪华品牌月度销售数据，针对多产品、多市场、多层次的汽车需求进行预测。方法结合点预测与概率预测，在战略与运营层面协同应用，采用轻量级梯度提升（LightGBM）集成模型，利用合并训练集、分位数回归及混合整数线性规划（MILP）校正方法实现跨层级一致性。结果表明，时空依赖关系与四舍五入偏差对预测精度影响显著，强制输出整数解可增强运营可行性；Shapley分析显示，短期需求受生命周期阶段、自回归动量与运营信号驱动，中期需求则反映在线参与度、规划目标与竞争指标等前瞻因素，其中在线行为数据在细分层级上显著提升预测准确率。
原文摘要 · Abstract (English)
Premium automotive manufacturers face increasingly complex forecasting challenges due to high product variety, sparse variant-level data, and volatile market dynamics. This study addresses monthly automobile demand forecasting across a multi-product, multi-market, and multi-level hierarchy using data from a German premium manufacturer. The methodology combines point and probabilistic forecasts across strategic and operational planning levels, leveraging ensembles of LightGBM models with pooled training sets, quantile regression, and a mixed-integer linear programming reconciliation approach. Results highlight that spatiotemporal dependencies, as well as rounding bias, significantly affect forecast accuracy, underscoring the importance of integer forecasts for operational feasibility. Shapley analysis shows that short-term demand is reactive, shaped by life cycle maturity, autoregressive momentum, and operational signals, whereas medium-term demand reflects anticipatory drivers such as online engagement, planning targets, and competitive indicators, with online behavioral data considerably improving accuracy at disaggregated levels.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。