# Automotive OEM Demand Forecasting: A Comparative Study of Forecasting Algorithms and Strategies
## 2 Related Work
[21] developed an ANN model considering the inflation rate, pricing changes in crude oil, and past sales. They trained and evaluated the model on data from the Maruti Suzuki Ltd. company from India, measuring the models' performance with the Mean Squared Error (MSE) metric. Ref. [27] compared three models: ANFIS, ANN, and a Linear Regression. They are trained on car sales data from the Maruti car Industry in India and compared with the root mean squared error (RMSE) metric. The authors report that the best performance was obtained with the ANFIS model. Ref. [28] compared the ARIMA and the Holt-Winters models using demand data regarding remanufactured alternators and starters manufactured by an independent auto parts remanufacturer. They measured performance using the MAPE and average cumulative absolute percentage errors (CAPE) metrics, obtaining the best results for the Holt-Winters models. Ref. [29] developed three models (ANN, Linear Regression, and Exponential Regression) based on data from the Kia and Hyundai corporations in the US and Canada. Results were measured with the MSE metric, obtaining the best one with the ANN model. Ref. [19] analyzed the usage of genetic algorithms to tune the parameters from ANFIS models built with data from the Saipa group, a leading automobile manufacturer from Iran. Measuring RMSE and R\({}^{2}\), they achieved the best results with ANFIS models tuned with genetic algorithms compared to ANFIS and ANN models without any tuning. Ref. [30] compared custom deep learning models trained on real-world products' data provided by a worldwide automotive original equipment manufacturer (OEM). Ref. [31] developed an long short-term memory (LSTM) model based on car parts sales data in Norway and compared it against Simple Exponential Smoothing, Croston, Syntetos-Boylan Approximation (SBA), Teunter-Syntetos-Babai, and Modified SBA. Best results were obtained with the LSTM model when comparing models' mean error (ME) and MSE. Ref. [22] developed tree models (autoregressive moving average (ARMA), Vector Autoregression (VAR) model, and the Vector Error Correction Model (VECM)) to forecast automobile sales in China. The models were compared based on their performance measured with RMSE and MAPE metrics, finding the best results with the VECM model. The VECM model was also applied by [20], when forecasting cars demand for the state of Sarawak in Malaysia. Finally, Ref. [32] compared forecasts obtained from different moving average (MA) algorithms (simple MA, weighted MA, and exponential MA) when applied to production and sales data from the Gabungan Industri Kendaraan Bermotor Indonesia. Considering the Mean Absolute Deviation, the best forecasts were obtained with the Exponential Moving Average. Additional insights regarding demand forecasting can be found in research related to time series forecasting in other domains. Refs. [33; 34] described the importance of time series preprocessing regarding trend and seasonality, though [35; 36] found the ANN models could learn seasonality. The use of local and global forecasting models for time series forecasting was researched in detail by [35]. Local forecasting models model each time series individually as separate regression problems. In contrast, global forecasting models assume there is enough similarity across the time series to build a single model to forecast them all. Researchers explored the use of global models either clustering time series [36; 37], or creating a single model for time series that cannot be considered related to each other [38]. They achieved good performance in both cases.