Automobile demand forecasting: Spatiotemporal and hierarchical modeling, life cycle dynamics, and user-generated online information

TL;DR

This paper develops a hierarchical, spatiotemporal demand forecasting model for automobiles, integrating online data and life cycle insights to improve accuracy across multiple levels and time horizons.

Contribution

It introduces a novel ensemble approach combining LightGBM, probabilistic forecasting, and MILP reconciliation for multi-level automotive demand prediction.

Findings

Spatiotemporal dependencies significantly impact forecast accuracy.

Online behavioral data enhances medium-term demand predictions.

Integer forecasts are crucial for operational feasibility.

Abstract

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.