Which machine learning technique revolutionized retail inventory forecasting by reducing prediction errors by over 30% compared to traditional methods?

Scientific advancements have revolutionized retail analytics through machine learning. One particular innovation has dramatically improved inventory management by predicting product demand with unprecedented accuracy. This technology combines multiple data sources and advanced algorithms to help science-focused retailers minimize waste and maximize efficiency. Test your knowledge about this cutting-edge retail science application!

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Long Short-Term Memory (LSTM) neural networks that incorporate seasonal patterns and external variables like weather and local events
Random Forest algorithms that classify products based on historical sales data and customer demographics
K-means clustering that groups similar products to predict collective demand fluctuations
Support Vector Machines (SVM) that map inventory levels against promotional activities