# Automotive OEM Demand Forecasting: A Comparative Study of Forecasting Algorithms and Strategies
## 3 Methodology
### Modeling  
#### 3.4.1 Feature Analysis and Prediction Techniques  
ML algorithms may have different requirements regarding data preprocessing in order to ensure the best learning conditions. We thus analyzed data distributions and identified which steps were required in each case to satisfy those requirements. For ML algorithms, we standardized (3) the features so that they would have zero mean and unit variance Equation (3), except for the case of the Multiple Linear Perceptron Regressor (MLPR), where we scaled the values of the features between zero and one Equation (4). Standardization enhances the model's numerical stability, makes some \begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Feature** & **Brief Description** \\ \hline \(wdp_{3 mt}\) & Estimate of target demand based on average demand per working day on third month before predicted month, and amount of working days on target month. \\ \hline \(sp\cdot\frac{demand_{output_{2}}}{sp_{moving}}\) & Planned sales for target month adjusted with ratio of weighted averages of past demand and past planned sales for given month. \\ \hline \(demand_{log4m}\cdot\frac{UE_{3m}}{UE_{3m}}\) & Lagged demand (4 months before target month), adjusted by the ratio of unemployment rates three, and fifteen months before the month we aim to predict. \\ \hline \(sp_{log12m}\) & Planned sales for last year, same month we aim to predict. \\ \hline \(sp\cdot\frac{UE_{3m}}{UE_{3m}}\) & Planned sales, adjusted by the ratio of unemployment rates three and fifteen months before the month we aim to predict. \\ \hline \(sp\) & Planned sales for target month \\ \hline \(demand_{log3m}\cdot\frac{GDP_{3m}}{GDP_{3m}}\) & Lagged demand (3 months before target month), adjusted by the ratio of GDP three and fifteen months before the month we aim to predict. \\ \hline \(sp\cdot\frac{GDP_{3m}}{GDP_{3m}}\) & Planned sales for target month, adjusted by the ratio of GDP three and fifteen months before the month we aim to predict. \\ \hline \(demand_{log3m}\) & Lagged demand (3 months before target month) \\ \hline \(wdp_{12}\cdot\frac{sp}{sp_{moving}}\) & Estimate of target demand based on average demand per working day a year before the predicted month and amount of working days on target month. Adjusted by the ratio between planned sales for target month and the weighted average of planned sales for the same month over past years. \\ \hline \(wdp_{8m}\) & Estimate of target demand based on average demand per working day on eighth month before predicted month and amount of working days on target month. \\ \hline \(wdp_{5m}\cdot\frac{UE_{3m}}{UE_{3m}}\) & An estimate of target demand based on average demand per working day on the fifth month before predicted month and amount of working days on target month. Adjusted by the ratio of unemployment rates three and fifteen months before the month we aim to predict. \\ \hline \(wdp_{12m}\cdot\frac{PMI_{3m}}{PMI_{3m}}\) & An estimate of target demand based on average demand per working day a year before predicted month and the amount of working days on target month. Adjusted by the ratio between PMI values 13, and 14 months beforethe target month. \\ \hline \(demand_{log3m_{total}}\) & Lagged demand (3 months before target month) - scaled between 0–1, considering products past demand values. \\ \hline \(wdp_{3m}\cdot\frac{GDP_{3m}}{GDP_{3m}}\) & Estimate of target demand based on average demand per working day on third month before predicted month, and amount of working days on target month. Adjusted by the ratio of GDP three and fifteen months before the month we aim to predict. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Top 15 features selected by the GBRT model considering the last three years of data. We did not remove correlated features in this case. algorithms consider all features equally important, and shortens ML models' training times [55]. \[x_{scaled}=\frac{X-\overline{X}}{\sigma} \tag{3}\]  
\[x_{scaled}=\frac{X-min(X)}{max(X)-min(X)} \tag{4}\]  
We took into account 21 forecasting techniques.