r/MachineLearning - [D] How are you all thinking about demand modeling in this pandemic?

TLDR: Given how important the global supply chain is right now, how might we help retailers and CPG companies to keep food on our shelves? Demand forecasting is back in the spotlight these days as Retail and CPG companies struggle to keep up with run-away demand for certain products in our new WFH normal. I'm sure many of you are also being asked about this problem, and I'm wondering how you all are forecasting demand in this environment. COVID-19 qualifies as a'black swan' event: the distribution of product-store-time sales has fundamentally changed over the past two weeks. Ensemble models (e.g., XGBoost, LightGBM) are likely to underfit if trained on historical data, while time-series models are likely to overfit due to high variance within product segments over the past few weeks Many retailers are seeing massive upswings in demand for specific products, but it's hard to tell whether these trends represent a'new normal' level of demand vs. widespread panic purchases that are unlikely to repeat themselves (e.g., the toilet paper craze) How are you thinking about approaching this problem?