## 3 Methodology
### Feature Extraction
#### 3.3.1 Statistical Features  
Inventory data contain statistical features that reflect the distribution, variability, and overall characteristics of the data. It uses advanced forecasting techniques that analyze seasonal trends and external factors like weather patterns. It helps to make predictions about the feature based on past data. The following is a description of each statistical feature:  
1. Mean: The mean represents the typical value or central tendency of inventory-related data over a specified period of time. It is calculated as the average after processing the data. \[ME=\frac{1}{d}{\sum}_{j=1}^{d}S_{e}^{*}\] (4)  
Hence, the dataset denotes the position of data points \(j\) and the overall number of elements \(d\), and the preprocessed output is denoted as \(S_{e}^{*}\).  
2. Variance: Variance indicates the degree of dispersion of the inventory data points from the mean. A greater variance simply points to a higher variability in the data. \[VA=\frac{1}{d}{\sum_{j=1}^{d}}(S_{e}^{*}-ME)^{2}\] (5) 3. Standard Deviation: The standard deviation is determined by taking the square root of the variance, indicating the usual amount of deviation of data points from the mean. It measures the extent of the dispersion or scatter in the dataset. \[SD=\sqrt{\frac{1}{d}{\sum_{i=1}^{d}}(S_{e}^{*}-ME)^{2}}\] (6) 4. Skewness: Skewness of inventory data measures asymmetrical distribution with respect to the mean of these data. A positive skewness means that the distribution of a given data point has a longer tail on the right side of the skewness, while a negative skewness means that the tails of the distribution of a given data point are extended on the left side of the skewness. \[SK=\frac{\frac{1}{d}{\sum_{j=1}^{d}}(S_{e}^{*}-ME)^{3}}{\left(\frac{1}{d}{ \sum_{j=1}^{d}}(S_{e}^{*}-ME)^{2}\right)^{3/2}}\] (7) 5. Kurtosis: Kurtosis is the measure of the peakedness or flatness degree of the inventory data distribution compared to being normally distributed. Larger kurtosis describes a sharper peak and larger tails, while smaller kurtosis indicates a flat distribution. \[KUI=\frac{\frac{1}{d}{\sum_{j=1}^{d}}(S_{e}^{*}-ME)^{4}}{\left(\frac{1}{d}{ \sum_{j=1}^{d}}(S_{e}^{*}-ME)^{2}\right)^{2}}\] (8) 6. Entropy: Entropy quantifies the uncertainty or irregularity in the probability distribution of inventory data. It provides information about the uncertainty or unordered data set. \[E(d)=-{\sum_{j=1}^{d}}\,p(S_{e}^{*})\log_{2}p(S_{e}^{*})\] (9) Here, \(p(S_{e}^{*})\) represents the probability mass function of \(d\). 7. Geometric Mean: The geometric mean is determined by finding the nth root of the total product of n numbers in the inventory dataset. This method is used to assess the central tendency of datasets that exhibit exponential growth or decay. \[GM=\left(\prod_{j=1}^{d}p(S_{e}^{*})\right)^{\frac{1}{d}}\] (10) 8. Harmonic Mean: Harmonic mean is the reciprocal of the arithmetical mean of the reciprocals of the inventory data values. It is quite useful for computing averages such as the rate or ratios. \[HM=\frac{d}{\sum_{j=1}^{d}\frac{1}{S_{e}^{*}}}\] (11)Minimum: The minimum value corresponds to the smallest data point in the inventory preprocessed dataset. It gives the lowest observed value. \[Min=\min(S_{1}^{*},S_{2}^{*},\ldots\ldots\ldots\ldots S_{n}^{*})\] (12) Maximum: The maximum value stands for the largest data point in the inventory preprocessed data set. It determines the peak value of the considered phenomenon. \[Max=\max(S_{1}^{*},S_{2}^{*},\ldots\ldots\ldots S_{n}^{*})\] (13) Sum: The sum is actually a sum of inventory data values over a given time period. It offers the integrated value of the whole dataset. \[SU=\sum_{j=1}^{d}S_{n}^{*}\] (14) The outcome obtained from the statistical feature is denoted as \(S=[ME//VA//SD//SK//KU//E(c)//GM//HM//max//min//SU]\).