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Online Webinair by Anne-Flore Elard

“If dataset A can be ingested on top of the data already used by the Machine Learning models, the forecast results would be 10% more accurate right?. Then if I add dataset B, it will add 5% additional accuracy so with the whole data we should get 95% accuracy.” “Why would I need to spend time on basic statistical forecast when I can go straight to Machine Learning models?” Here are two sample quotes I hear almost every day rooted in the concept that Machine Learning models applied to forecasting are superior to statistical forecasting models in that they are intrinsically more accurate. In this perhaps iconoclastic discussion, I will explore the value-add of Machine Learning for industry-based forecasts. First, let’s look at some research and review the accuracy obtained from different methods in the case of highly random data or specific patterns. Second, as a practitioner, I will spend time discussing the cost component of Machine Learning implementations, both quantitatively and qualitatively with interpretability. Last but not least, I will highlight some lessons learnt over the years on how to effectively implement Machine Learning methods to industry-focused forecasts.