Hacking the TSB Model for Intermittent Time Series to Accommodate for Availability Constraints

In many demand forecasting problems, we face the challenge of predicting the demand for a product or service with very sparse data. This is especially true in retail, where many products are only sold occasionally. This sparsity can come from many sources, as greatly described in the (recommended!) blog post “Why zeroes happen” by Ivan Svetunkov . On one hand, the sparsity can simply come from the…