Automobile demand forecasting: Spatiotemporal and hierarchical modeling, life cycle dynamics, and user-generated online information
This addresses forecasting challenges for premium automotive manufacturers facing high product variety and volatile markets, but it is incremental as it applies existing methods like LightGBM to new data.
The study tackled monthly automobile demand forecasting for a German premium manufacturer by combining point and probabilistic forecasts across strategic and operational levels, using ensembles of LightGBM models and a reconciliation approach. Results showed that spatiotemporal dependencies and rounding bias significantly affect accuracy, with online behavioral data improving forecasts at disaggregated levels.
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.