M5 Forecasting Conference on Dec 6-7 is now virtual
While originally intended to be held in New York City, global conditions surrounding Covid have shifted the M5 Forecasting Conference to a virtual event. Although the circumstances are unfortunate, the glass half full take is that the conference is now open to a much larger audience. The last M conference was held back in 2018 and I found the experience extremely valuable (I wrote about it here). If you have some free time next week, I highly recommend attending some of the sessions. It is a great, free opportunity to learn from leaders in the forecasting community.
The M5 Conference Agenda includes distinguished speakers from the major software/technology companies (Google, Kaggle, Walmart, Microsoft, Amazon, Facebook, SAS and Target), as well as leading academics from top-tier universities. It features the presentation of the three most accurate methods of the M5 Competition by the developers themselves who will also discuss how their methods can be implemented by others. Their code will be available for free on GitHub. The Conference covers all critical aspects of forecasting, including combining methods and introducing judgmental adjustments, paying special emphasis on the comparison of Machine Learning and Statistical forecasting methods as well as the assessment of uncertainty.
Who should attend?
Professionals working in companies or non-profit organizations in jobs that involve preparing forecasts and estimating uncertainty
Financial managers who prepare budgets and the financial requirements for their firms
Government officials requiring to predict receipts and expenses
Hedge fund and other related managers who need to predict stock and other market variables
Production managers requiring forecasts for their production planning activities
Inventory managers who must predict the demand for a large number of items to figure out optimal inventory levels and reordering points
Logistics and transportation managers whose scheduling tasks need forecasts
Academics teaching forecasting and related courses
Students interested in forecasting for their courses or for their research