# Demand Forecasting in Supply Chain Using Uni-Regression Deep Approximate Forecasting Model
## 3 Proposed Methodology
### Feature Extraction
#### 3.3.4 Demand Indicator Feature  
The demand indicator feature is a specific attribute or characteristic that has been derived from an analysis of historical data regarding sales or demands. It is required to use demand indicators to understand the pattern of demands for certain products or items over time. These characteristics play a significant part in the prediction of future needs and informed decision-making within supply chain management. The forms of demand indicators may differ, and most often they are extracted from the analysis of sales data in history. Some common demand indicators include:  
(a) Sale Frequency (SF)  
SF stands out as a significant measure in business analysis and refers to the mean frequency of sales transactions occurring within any set period. SF is a quantitative measure of the tempo of sales activities and is calculated by dividing the total number by total time durations. This metric is useful in measuring the frequency and intensity of customer transactions, providing critical information about demand patterns for a company's offerings. In addition, the seasonality of market signals is particularly well analyzed by SF. Through SF changes under different time intervals, businesses can find correlations between high and low seasons. These data become crucial for strategic planning, enabling companies to synchronize their marketing campaigns and various inventory operations as well as coordinating the entire organization with customer demand. In essence, sale frequency is a dynamic metric that enables businesses to make informed decisions, ensures efficient utilization of resources, and facilitates adaptability in light of market dynamics.  
\(SF\) is calculated by dividing the total number of sales \((S)\) by the total time period \((T)\).  
\[SF=\frac{N}{T} \tag{12}\]  
In this case, \(N\) symbolizes the total number of times that \(S_{i}\) was on top in supply chain delivery, while \(T\) denotes the overall product within a retail supplier firm.  
(b) Inverse Sale Frequency (ISF)  
ISF in the retail supply chain is an important metric that marks product sales rarity within a given period. ISF is calculated by multiplying the total supply of a product by both seasonal and non-seasonal demand, which tells us how often it sells. A higher value of ISF means lower sales frequency, implying that a product is exclusive or has a slower turnover rate. This metric carries important implications for inventory management, as it helps retailers to recognize products that have unique market positions. Strategically, a higher ISF may make retailers concentrate on creating perceived value or dynamic marketing initiatives that stimulate demand as compared with the results of a lower ISF, which directs inventory optimization strategies based upon persistent demand levels. Basically, inverse sale frequency is a helpful tool to give retailers insight into whether to adjust inventory and marketing methods depending on the specificity of demand for each product in dynamic business conditions.  
\[ISF=\log\left(\frac{P}{S}+\frac{P}{NS}\right) \tag{13}\]  
In this case, \(P\) is the total product in the retail supply chain, \(S\) indicates the seasonal demand of \(S_{i}\) during the sale, and \(NS\) represents non-seasonal sale demand.