Değişken otokodlayıcı kullanarak envanter stoğundaki anomalilerin tespiti

Retail companies monitor inventory stock levels regularly and manage stock levels based on forecasted sales to sustain their market position. The accuracy of inventory stocks is critical for retail companies to create a correct strategy. Many retail companies try to detect and prevent inventory record inaccuracy caused by employee or customer theft, damage or spoilage and wrong shipments. Our study aimed to detect inaccurate stocks using the Variational autoencoder (VAE) method, and we used the real inventory stock data of one of Turkey's largest supermarket chains. This method learns the distribution of data, and it is a great advantage to use this in data that changes over time. The VAE learns the usual pattern from normal time series data and detects anomalies by identifying the unseen data pattern, possibly reducing time and effort while gathering error data. In addition, this method can be applied to any product level. However, we use the interquartile range method to define the threshold for our model; therefore, it becomes parametric. On the other hand, generally, researchers use public data to develop methods, and it is challenging to apply machine learning algorithms to real-life data, especially in unsupervised learning. We show how to handle real-life data noises, missing values etc. The experimental findings show that the proposed approach can detect anomalies in the low and high inventory stock quantity and quickly apply to other time series anomaly detection problems.