# Automotive OEM Demand Forecasting: A Comparative Study of Forecasting Algorithms and Strategies  
[PERSON]  
1 Jozef Stefan Institute, Jamova 39, 1000 Ljubljana, Slovenia; [EMAIL_ADDRESS] (B.K. ); [EMAIL_ADDRESS] (M.S. ); [EMAIL_ADDRESS] (B.F.); [EMAIL_ADDRESS] (D.M.) 2 Qeterod.o.o.o.o.o.o.o.o.o.o.o.o.o.o.o.o.o.o.o.o.o.o.o.o.o.o.o.o  
per target demand or a global model to address them all. When designing the models, it is crucial to consider which data is potentially relevant to such forecasts. Multiple factors may affect the product's demand. First, it is necessary to understand if the products can be considered inelastic (their demand is not sensitive to price fluctuations), complementary, or be substituted for alternative products. Second, intrinsic product qualities, such as being a perishable or luxury item, or their expected lifetime, may be relevant to demand. Finally, the manufacturer must also consider the kind of market it operates on and the customer expectations. When dealing with demand forecasting in the automotive industry, most authors do not use only demand data but also incorporate data regarding exogenous factors that influence demand. Demand forecasting has direct consequences on decision-making. As such, forecasts are expected to be accurate so that they can be relied upon. When training machine learning models, more significant amounts of good quality data can help the model better learn patterns and provide more accurate forecasts. This intuition is considered when building global models. On the other side, while for local models, the forecast error is constrained to past data of a single time series, in global models, the forecasting error is influenced by patterns and values observed in other time series, which can lead to greater errors as well. In this work, we explore a strategy to constrain such forecasting errors in global models. Furthermore, providing a greater amount of data should be considered regarding products' demand and its context. To that end, we enrich the demand data with data from complementary data sources, such as world Gross Domestic Product (GDP), unemployment rates, and fuel prices. This work compares 21 statistical and machine learning algorithms, building local and global forecasting models. We propose two data pooling strategies to develop global time series models. One of them successfully constrains the global time series models' forecasting errors. We also propose a set of metrics and criteria for the evaluation of demand forecasts for smooth and erratic demands [12]. The error bounding data pooling strategy enables us to gain the benefits of training machine learning models on larger amounts of data (increased forecast accuracy) while avoiding anomalous forecasts by constraining the magnitude of maximum forecasting errors. The metrics and evaluation criteria aim to characterize the given forecasts and provide better insight when deciding on the best-performing model. We expect the outcomes of this work to provide valuable insights for the development and assessment of demand forecasting models related to the automotive industry, introducing forecasting models and evaluation strategies previously not found in the scientific literature. To evaluate the performance of our models, we consider the mean absolute scaled error (MASE) [13] and the R\({}^{2}\)-adjusted (R\({}^{2}\)adj) metrics. We compute the uncertainty ranges for each forecast and compare if differences between forecasts are statistically significant by performing a Wilcoxon paired rank test [14]. Finally, we analyze the proportion of products with forecasting errors below certain thresholds and the proportion of forecasts that result in under-estimates. The rest of this paper is structured as follows. Section 2 presents related work. Section 3 describes the methodology we followed to gather and prepare data, create features, and build and evaluate the demand forecasting models. Section 4 details the experiments performed and the results obtained.