It’s always fun to watch business consultants and IT companies search for the next buzzword - throwing candidates to the wall, hoping that one of them will stick and help them own the next big thing. (IBM is particularly adept at the game, rotating buzzwords every two years.)
Data scientists are waking up to the fact that combining multiple, relatively simple predictive models is often more efficient, when tackling a time series forecasting challenge, than using the latest super-duper neural network.
In a previous post, we explained the concept of cross-validation for time series, aka backtesting, and why proper backtests matter for time series modeling.
Our goal in this post is to discuss our standard strategy (beyond respecting basic time series modeling principles) for building accurate predictive models. We will use the example of commodity procurement optimization.
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…
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:
