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.
Seasonality - recurring but not necessarily periodic data patterns - is a staple of time series modeling. Since capturing true seasonality greatly enhances model accuracy, we wanted to share our thoughts and experience on the detection and modeling of such data patterns.
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.