Supply Chain Anomaly Detection and Prediction Models Based on Large Scale Time Series Data

In supply chain management, the accuracy of demand forecasting is essential to optimize inventory and improve operational efficiency. However, there are often outliers in the demand data (such as sudden demand changes or human error) that, if not effectively addressed, can seriously interfere with the forecasting model. This study discusses how to improve the accuracy of supply chain demand forecasts by anomaly detection and treatment. The IQR is used to detect and smooth the abnormal demand data, and the demand prediction model is used to correct it. The experimental results show that the accuracy of demand prediction after smoothing is improved, especially when the demand changes and fluctuations are captured. KPI analysis shows that the prediction model after anomaly detection is better than the unprocessed data on RMSE and MAE indicators. This method provides an effective solution for demand forecasting for dealing with large fluctuations or abnormal data and can optimize supply chain management decisions.