The Datapred blog

(Based on too many true stories) You are the Operations Manager of an industrial company. You have read a lot about Industry 4.0, and asked your favorite consultancy to build a predictive maintenance prototype for your key industrial asset.

Machine learning for demand prediction is all the rage: industrial companies are suddenly waking up to the potential of machine learning in that area, proofs of concept are being launched everywhere, consulting companies are making millions…

Time series modeling requires the understanding of concepts that feel both basic and abstract:

Industry is finally waking up to the potential of machine learning for predictive analysis. But data scientists faced with a prediction challenge (demand prediction, predictive maintenance, churn prediction…) usually favor one of two equally inadequate approaches:

Our definition of times series is: sequentially revealed, time-stamped and time-critical data.