## 3 Materials and Methods
### Data Preprocessing
#### 3.1.2 Feature Construction  
To ensure the model's performance in medium- and long-term predictions, this study constructs features that exert significant influence over extended time spans [19]:  
(1) Time Dimension: Seasonal and holiday features were extracted to effectively capture the cyclical changes in consumption patterns and sales fluctuations. These features are particularly significant in explaining sales variability over longer time spans [20]. (2) Geographic Dimension: By utilizing the latitude and longitude data of different regions, the spatial distribution of consumption potential and market demand over the medium to long term can be revealed. These geographical features help the model better \begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Related Work** & **Methods/Techniques** & **Limitations** & **Contribution** & **Contribution Improvement** \\ \hline [PERSON] & & Too simple, unable to capture complex non-linear relationships and multidimensional factors & Proposed STL-DCSInformer-ETS model, combining STL decomposition, DCSInformer, and ETS, to capture short-term fluctuations and long-term trends & Can handle more complex multidimensional data, accurately capturing seasonal fluctuations and long-term trends & Can handle more complex multidimensional data, accurately capturing seasonal fluctuations and long-term trends & Can handle more complex multidimensional data, accurately capturing seasonal fluctuations and long-term trends \\ \hline [PERSON] et al. (XG-Boost + GA) [4] & XGBoost with Genetic Algorithm optimization & Sensitive to long-term trend changes, struggles with long-term prediction tasks & Introduced the DC-SInformer model, overcoming long-term trend prediction issues with dilated causal convolution and multi-scale feature extraction & Improved long-term sales prediction accuracy, reduced sensitivity to long-term trend changes \\ \hline [PERSON] et al. (Informer) [17] & Informer & Faces challenges in long-term trend variation when handling multi-step forecasting & Added RF-RFE method for feature selection, optimizing feature selection precision & Improved feature selection accuracy, enhancing model predictive performance and stability \\ \hline [PERSON] et al. (OCCCPH-MHA) [12] & Multi-stage deep learning (OCCPH-MHA) & Limited in capturing short-term fluctuations and long-term trends effectively & Combined STL decomposition and ETS models to capture seasonal fluctuations and long-term trends effectively & Increased accuracy in capturing both short-term fluctuations and long-term trends \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of related work and significant contributions. capture regional differences in consumer behavior, enhancing the spatial adaptability of the predictions. (3) Economic Dimension: Macro-economic indicators, such as consumer confidence indices, national policies, and government spending, were analyzed. In medium to long-term forecasting, macro-economic features are crucial in reflecting overall consumption demand, effectively capturing long-cycle market fluctuations [21]. (4) Product Dimension: Features such as packaging format, advertising investment, store promotion frequency, and supply-chain delays were extracted. These help the model identify sales trends throughout a product's lifecycle, particularly clarifying key stages of the lifecycle in medium to long-term forecasting. (5) Weather Dimension: Meteorological features such as temperature and rainfall were integrated to analyze the short-term and long-term effects of weather conditions on sales. Especially in markets with significant seasonality, weather features have an undeniable impact on medium- to long-term sales fluctuations [22]. Table 2 shows the features and their meanings. By incorporating these features, the model can accurately capture short-term sales fluctuations while effectively reflecting medium- and long-term trend variations.