ML-Based Time Series Regression: 10 concepts we learned from Demand Forecasting
By using demand forecasting as example, I will introduce various crucial concepts for making time series predictions with machine learning models, ranging from feature engineering and preprocessing to explainable and causal ML.
Felix Wick received his PhD in high energy physics at the Karlsruhe Institute of Technology in 2011. For several years, he then led the machine learning team at Blue Yonder, a provider of cloud-based predictive applications focussing on the retail market, and developed new methods for demand forecasting and causal inference. After acquisition of Blue Yonder by JDA, a leading provider of supply chain management software, and subsequent re-branding of the merged company to Blue Yonder, he now runs the data science and machine learning R&D organization, and strives for construction of an autonomous supply chain powered by artificial intelligence.