CMAF FFT: Machine Learning forecasts – confirmation bias or value add?
Channel: Lancaster CMAF
Date: 2023-01-18

This is the fourth webinar in the 3 Season of "Friday Forecasting Talks", hosted by Centre for Marketing Analytics and Forecasting of Lancaster University, UK.
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*Content of this video*
00:00 - Introduction
02:41 - Presentation by Anne-Flore Elard
08:50 - When to use Machine Learning for Forecasting
27:00 - What Does it Mean to Productionize ML
42:35 - Comments by Nikos Kourentzes and Q&A

The abstract: “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.

Speaker: Anne-Flore Elard

Bio: Anne-Flore Elard is a practitioner in data, data science and analytics, trained in Statistics and Management with an MBA from MIT Sloan. Driven by the value-add that well-managed data and data science can bring to businesses, she has led ML/AI services teams at Deloitte, Scotiabank and Kinaxis. Today, she works at Kinaxis on a portfolio of advanced analytics innovations with the goal to bring them to the market.