Fashions for Demand Prediction
Initially, univariate time sequence fashions equivalent to Prophet, Darts, Sktime have been used to foretell demand. Nonetheless, the outcomes weren’t adequate resulting from inadequate knowledge factors (solely 3–4 years of information). This method additionally required extra mannequin administration since every merchandise in every retailer is represented by one time-series mannequin (in most shops, there are over 100+ objects).
Machine studying fashions, equivalent to Random Forest, XGBoost, and Gentle Gradient-Boosting Machine (LBGM) have been then examined the place one mannequin could possibly be used for all shops. Nonetheless, the outcomes have been nonetheless unsatisfactory. The identical fashions have been explored once more, however with one mannequin representing one retailer in comparison with when one mannequin was used for all shops.
The outcomes when utilizing one mannequin for one retailer confirmed an enchancment. However, managing the fashions for every retailer nonetheless proved to be fairly difficult. Thus, an method that makes use of clusters (one mannequin for 3–8 shops) was used to beat this downside. The clusters have been chosen by analyzing the gross sales gross merchandise worth (GMV) and the objects being bought in every retailer. On the finish of the trial, I discovered that Random Forest with a number of clusters within the configuration produced the perfect end result.
